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LinkedIn Jobs API
LinkedIn job postings — title, company, location, description, salary and full company profile. Search by job title, location and filters.
Sample
Sample data
Here's a sample run of job postings across roles — engineering, legal, sales, healthcare — showing the exact schema and results you can expect, including salary, recruiter, and benefits where listed.
| # | Job URL | Job ID | Job Title | Description | Employment Type | Workplace Type | Remote Allowed | Experience Level | Applicants | Posted | Expires | Apply URL | Easy Apply | Industries | Salary Min | Salary Max | Salary Currency | Salary Period | ATS | Benefits | Recruiter Name | Recruiter URL | City | State | Country | Company Name | Company ID | Company Slug | Company URL | Company Logo | Company Website | Company Employees | Company Followers |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1 | 4454227571 | AI Engineer, LLM Inference & AI Agents | WorkorAI is recruiting on behalf of an early-stage AI infrastructure company building a coding agent that creates and improves an entire LLM inference stack: GPU kernels, runtime, and serving infrastructure.
This is a senior engineering role for someone whose experience combines production AI agents with either LLM inference systems or GPU kernel development.
About the role
The company is developing an agent that receives specifications and test results, writes low-level code, runs it against real hardware or simulation, analyzes failures, and keeps iterating until strict correctness and performance gates pass.
There is no room for plausible-looking output that does not work. Every component is validated against reference implementations, test suites, and hardware benchmarks.
On its first target, a proprietary accelerator with no existing inference ecosystem, the system reached working tensor-parallel matrix multiplication in approximately 10 hours and ran three frontier models end to end within 10 days. It is now serving production traffic.
What you will do
• Build the central agent controller that writes, executes, evaluates, and improves inference code.
• Design evaluation loops that detect incorrect output, compare implementations against references, and enforce performance gates.
• Own the test ladder used to validate every layer before other components are built on top of it.
• Orchestrate parallel work across kernels, runtime components, and serving infrastructure, including retry, dependency, escalation, and human-review logic.
• Review and validate low-level code produced by the agent against real hardware and simulation results.
What we are looking for
You have production experience building with LLMs or coding agents, including tool-use loops, constrained code generation, evaluation harnesses, and systems where tests catch model errors.
You also have meaningful depth in at least one of these areas:
• LLM inference systems: vLLM, KV cache, paged attention, continuous batching, quantization, speculative decoding, FlashAttention, or low-latency serving.
• GPU and accelerator kernels: CUDA, Triton, ROCm/HIP, Metal, attention, matrix multiplication, normalization, MoE, kernel fusion, or performance optimization.
Strong Python skills are required. You should also be comfortable reading or reviewing C++, Rust, CUDA C, or similarly low-level systems code.
Experience with LLVM, MLIR, TVM, compiler code generation, accelerator bring-up, NCCL, Megatron-LM, DeepSpeed, distributed inference, firmware, or non-GPU execution models is valuable but not required.
Why this role
• $200,000–$420,000 compensation range.
• San Francisco preferred; remote work can be discussed for a strong fit.
• Visa sponsorship may be available, including H-1B.
• $15M raised from investors focused on AI infrastructure and silicon.
• Approximately 14 engineers, with engineering and product operating as one team.
• Founded by a former Google Brain researcher.
• Production systems with measurable correctness and performance feedback rather than demo-only agent workflows.
How to apply
Apply through WorkorAI:
https://workorai.com/candidate/apply/cmsrca19o0001tpbqjf9vw7kl
The application includes a WorkorAI profile and a short role-focused AI interview. The complete process should take no more than 15 minutes.
WorkorAI is managing sourcing and initial technical evaluation for this search. | full_time | remote | true | — | 25 | 2026-08-17T03:23:22.000Z | 2026-09-16T03:23:22.000Z | false | — | — | — | — | — | — | — | — | — | — | — | United States | WorkorAI | 132333913 | workorai | 2 | 109 | |||||
2 | 4455212379 | Machine Learning Engineer | Founding Machine Learning Engineer (Equity-Based) – Robotics & Hand Dexterity StartupLocation: South Florida (Hybrid/Remote Considered)We are building a company focused on solving one of the most challenging problems in robotics: hand dexterity and object manipulation.Our technology uses a sensor-equipped glove to capture human manipulation data, creating a foundation for robotic learning, prosthetics, automation, and cross-embodiment intelligence. By combining motion capture, tactile sensing, force sensing, and robotic feedback, we aim to teach machines how humans interact with the physical world.While the company is only one week old, we have already seen significant traction, strong investor interest, and have been nominated as one of only 20 companies selected to attend the Florida Venture Forum's early-stage venture competition.We are currently pre-funding and focused on building a working prototype that will be demonstrated to investors as part of our fundraising efforts.What We're Looking ForWe are seeking an exceptional Machine Learning Engineer who can help transform raw manipulation data into intelligent robotic behaviors.This role will focus on teaching robots how to understand, predict, and replicate human dexterity using data collected from our sensor-equipped glove platform.ResponsibilitiesDevelop machine learning models that learn human hand dexterity and manipulation behaviors.Train models using motion, force, pressure, tactile, and sensor-fusion datasets.Learn and model object manipulation patterns across a wide range of tasks and environments.Develop cross-embodiment mapping systems that transfer human actions to robotic hands and other robotic platforms.Build models capable of predicting grasp intent and manipulation objectives before actions are completed.Design training pipelines for collecting, processing, labeling, and validating manipulation datasets.Develop imitation learning, reinforcement learning, and behavior-cloning approaches for robotic control.Collaborate with software, embedded systems, and mechanical engineers to create an end-to-end learning system.Evaluate model performance and continuously improve dexterity, precision, and generalization capabilities.Support the development of investor-ready demonstrations and proof-of-concept systems.QualificationsWe are not looking for average talent.To qualify, you must demonstrate exceptional engineering ability, creativity, adaptability, and problem-solving skills. Startup environments require individuals who can operate with limited resources, move quickly, and find solutions where others see obstacles.Ideal candidates will have experience in:Machine Learning and Deep LearningPyTorch, TensorFlow, or JAXRobotics and robotic manipulationReinforcement LearningImitation Learning and Behavior CloningComputer Vision and Sensor FusionMotion Capture and Human Activity RecognitionTime-Series Modeling and Sequential LearningData Pipeline DevelopmentPython and modern ML toolingLarge-scale model training and evaluationRobotics simulation environments such as Isaac Sim, MuJoCo, or Gazebo (preferred)Experience in robotic hands, prosthetics, dexterous manipulation, or cross-embodiment learning is highly desirable.The ideal candidate is someone who can transform large amounts of sensor data into intelligent robotic behavior and help create the next generation of dexterous robotic systems.CompensationThis is a founding-stage opportunity.Initially, compensation will be equity-based during a probationary period. Your primary objective will be helping us build a working demonstration prototype.Upon successful completion of this milestone and the securing of investment funding, the position is expected to transition into a salaried role while maintaining meaningful equity participation in the company.We believe the opportunity to solve hand dexterity at scale has the potential to become one of the most significant advancements in robotics, automation, prosthetics, and human-machine interaction. We are looking for someone who shares that vision and wants to help build something transformative from the ground up.If you are confident in your abilities, excited by difficult technical challenges, and interested in becoming a founding member of a high-potential robotics company, we'd love to hear from you.Apply by sending your resume, GitHub, research papers, portfolio, or examples of machine learning, AI, robotics, or data-driven projects you have built. | — | remote | true | — | 25 | 2026-08-16T23:25:17.000Z | 2027-02-12T23:25:17.000Z | true | — | — | — | — | — | LinkedIn | — | — | — | — | — | United States | PlainHand | 116052898 | plainhand | 5 | 174 | |||||
3 | 4455224054 | Senior AI automation engineer | Back to AI automation
AI automation
Senior AI automation engineer
Create internal workflow agents and automation systems for sales, support, and operations teams.
Example employerRemote (US)$175K-$220K
Role signals
EngagementFull-time
SenioritySenior
Fit signalSignals, not decisions
Employer reputationCollecting
Employer quality signals are still collecting.
Signals collecting
Apply to this roleHow fit signals workReport this role
Role overview
Example employer needs an AI automation engineer who can connect business workflows, agent tooling, and reliable operational handoffs. This is example content for the controlled launch experience.
90-day success
Within 90 days, replace two manual operational workflows with monitored AI-assisted automations.
Tools and skills
n8nLangChainPythonworkflow automationagent orchestrationsystems integration
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Check my resume match
Role overview
Example employer needs an AI automation engineer who can connect business workflows, agent tooling, and reliable operational handoffs. This is example content for the controlled launch experience.
90-day success
Within 90 days, replace two manual operational workflows with monitored AI-assisted automations.
Tools and skills
n8nLangChainPythonworkflow automationagent orchestrationsystems integration
Ready to apply?
Your candidate profile helps employers review tools, proof sources, and work preferences alongside this application.
Start application
See how your resume matches this kind of role
Paste your resume and a job description to get an explainable match score before you apply.
Check my resume match | full_time | remote | true | — | 25 | 2026-08-16T19:18:19.000Z | 2026-09-04T09:01:13.000Z | false | — | — | — | — | — | — | — | — | — | — | United States | AppliedHire | 135294748 | appliedhire | 1 | 4 | ||||||
4 | 4454230129 | AI Researcher | About UsInOrbit.AI is a leading provider of robot operations (RobOps) software, enabling companies to manage, monitor, and optimize their robot fleets. Our mission is to maximize the potential of every robot, helping businesses unlock new levels of efficiency and productivity. We believe in fostering a collaborative and innovative environment where every team member contributes to shaping the future of robotics, including the integration of agentic AI and physical AI to enhance robot capabilities.
The OpportunityWe are seeking a highly specialized and technically versatile AI Researcher to operate as a high-impact team member. This unique role combines deep AI research with robust production-grade ML engineering and full-stack system development. You will be driving critical AI features, responsible for the end-to-end lifecycle—from foundational algorithm research and data pipeline construction to deploying scalable, user-facing applications that utilize the models. This role is best suited for an expert who can seamlessly transition between prototyping new AI models, architecting MLOps infrastructure, and developing APIs and interfaces.
Responsibilities:
Foundational Research & Novel Prototyping: Lead advanced, hands-on AI research to explore novel algorithms, generate intellectual property, and rapidly prototype experimental models that directly inform and unlock new product capabilities.End-to-End System Architecture: Contribute to the design and implementation of the entire AI ecosystem, including the core ML model, data pipelines (MLOps), application APIs, and integration layers necessary to ship features.Full Stack Development & Integration: Develop and maintain the end-to-end software components required to integrate AI models into the InOrbit platform, including backend services, robust APIs, and user-facing features (where applicable).Production Model Deployment & MLOps: Independently manage the full AI lifecycle, ensuring high-performance, cost-effective, and secure deployment of models into production using best-in-class MLOps practices.Technical Authority & Standard Setting: Act as a technical authority in the AI domain, setting code quality, architectural standards, and operational excellence.Performance & Optimization: Lead efforts to identify and resolve performance bottlenecks, from model inference latency to API response times and data throughput.Cross-functional Collaboration: Partner with Product Management and core Engineering to translate complex technical concepts and research breakthroughs into shippable, business-driving features.
QualificationsPh.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field. Candidates who left a PhD program to found or join a startup are encouraged to apply.Minimum of 5 years of progressive, hands-on technical experience in a combination of AI Research, Machine Learning Engineering, and Full Stack/Systems Development.Proven track record of successfully leading/contributing to the technical architecture and delivery of complex, large-scale software systems driven by novel AI/ML models.Deep, hands-on expertise in advanced AI/ML techniques (e.g., reinforcement learning, agentic AI, deep learning) and expert-level proficiency in at least one modern full-stack programming language/framework.Strong understanding and practical experience with MLOps, cloud-based infrastructure (e.g., GCP, AWS, Azure), and data pipeline construction.Ability to solve ambiguous technical problems across the stack and drive major technical initiatives independently.Strong communication skills, with the ability to articulate architectural decisions and research findings to both technical and non-technical audiences.
Preferred QualificationsExtensive use of AI coding and design agents, strong spec-writing skills. Experience building and maintaining real-time systems and high-performance, low-latency APIs in a production environment.Contribution to open-source projects or peer-reviewed research in AI/ML.Experience in robotics, autonomous systems, or other forms of physical AI.
Compensation: We offer a competitive and flexible compensation package, considering a wide range of experience levels and geographic locations. The expected compensation for this role includes base salary, a performance-based bonus, benefits and equity.Your actual compensation will be determined by your skills, experience, and alignment with the scope of the role.
Candidates must possess current and valid work authorization for the United States. InOrbit.ai does not provide sponsorship for employment visas (including H-1B, OPT/CPT, TN, etc.) for this position, now or in the future.
| full_time | on_site | false | — | 25 | 2026-08-17T00:43:04.000Z | 2027-02-13T00:43:04.000Z | true | — | — | — | — | — | LinkedIn | — | — | — | San Francisco | California | United States | InOrbit.AI | 18373646 | inorbitai | 46 | 5,620 | |||||
5 | 4453738790 | Senior AI Engineer — Voice Systems | The company pairs Medicare beneficiaries with a dedicated healthcare advocate who navigates appointments, insurance, and care coordination on their behalf. Customers get the support of caring nurses while AI agents do the tedious backend work, all covered by Medicare. Today, 24/7 personalized health assistance is only available to the rich or extremely sick. Our vision is for everyone to be able to afford a health assistant who knows your health history deeply, navigates the healthcare system on your behalf, and propels you to become the healthiest version of yourself. Founded in 2025, backed by leading venture capital firms and top Silicon Valley angel investors. 9 employees, $6.5M total funding.
ResponsibilitiesAutomate tasks healthcare advocates currently do manually, including outbound voice calls to insurance, doctors, pharmacies, and patientsBuild and improve agentic search and multi-agent orchestration systems that coordinate across complex healthcare workflowsDesign evaluation infrastructure to measure and improve the quality of AI automations so advocates increasingly rely on themImplement reinforcement learning loops that use real nurse actions to train and improve models over timeShip fast and iterate directly with the team that listens to real conversations daily
Requirements1-10 years of experience in AI/ML engineering, building systems including voice AIHas shipped voice AI and agentic/LLM-powered systems to production (not demos)Production voice AI stack: streaming STT/TTS, telephony, latency optimization, endpointingAgentic systems: context engineering, multi-agent orchestration, tool use, RAGBuilt evaluation pipelines for voice systems - metrics, regression suites, production signalExperience as a founder or from a stalling later-stage company, or Big Tech with prior startup experienceRapid learner who thrives in ambiguity - builds the playbook rather than following oneWilling to relocate to SF in Q1 2027
Benefits$180K-$220K base salary plus competitive equity, higher for exceptional candidatesOpen to visa sponsorship (e.g. OPT, H1B transfers) | full_time | remote | true | Mid-Senior level | 25 | 2026-08-17T00:44:11.000Z | 2026-09-16T00:44:11.000Z | false | 180,000 | 220,000 | USD | YEARLY | — | — | — | — | San Francisco | California | United States | Jack | 100920821 | searchwithjack | 146 | 156,033 | ||||||
6 | 4453997368 | Senior AI Engineer | Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation. Apply today to express your interest in roles like this one.
Visit our website to learn more about our process and explore free tools for your job search, including our live job market dashboard with salary, skills and hiring trend data from thousands of AI transformation roles.
What The Market Looks Like
The market for specialist-level AI engineering talent remains active across the US, with 37,300+ postings in the last six months concentrated in California, New York, and Texas. Demand spans Professional Services, Technology, Financial Services, and Healthcare sectors. Compensation for this cohort typically ranges from $170K to $340K annually. The strongest candidates bring production experience shipping AI systems end-to-end—from model selection and training through deployment and monitoring—combined with software engineering discipline, practical knowledge of LLM tooling, and the ability to communicate complex technical decisions to non-technical stakeholders.
Job Responsibilities
Design, build, and ship AI/ML systems and features in production environments, owning quality and performance across the full lifecycleSelect and evaluate ML frameworks, model architectures, and deployment strategies based on business requirements and technical constraintsPartner with data scientists and product teams to translate research and prototypes into reliable, scalable systemsImplement monitoring, logging, and observability to track model performance, detect drift, and respond to production issuesIntegrate large language models, embeddings, and other AI/ML components into application workflows, managing latency and cost trade-offsLead code reviews and mentor junior engineers, establishing best practices for ML code quality and testingCollaborate with infrastructure and platform teams to optimize compute resources and ensure reproducible, auditable AI pipelines
Candidate Requirements
5+ years of professional software engineering experience, with at least 2–3 years actively building and deploying ML/AI systems in productionStrong fundamentals in Python, software architecture, and debugging; hands-on experience with ML frameworks (PyTorch, TensorFlow, or equivalent) and MLOps toolingDemonstrated ability to own a system end-to-end: from problem definition through model selection, training, evaluation, and monitoringExperience working with LLMs, retrieval-augmented generation (RAG), or fine-tuning in a production contextComfort explaining technical trade-offs and AI system behavior to product, business, and non-technical audiencesTrack record of shipping features or systems on schedule and collaborating effectively across teams in fast-moving environments | full_time | remote | true | Associate | 43 | 2026-08-16T18:59:52.000Z | 2026-09-15T18:59:52.000Z | false | — | — | — | — | — | — | — | — | — | — | United States | Axial Search | 107463842 | axialsearch | 1 | 782 | ||||||
7 | 4453992481 | AI Platform Support Engineer | About Our ClientThe organization operates in the utility bill auditing and cost recovery industry, having delivered more than $700 million in refunds and savings to clients over more than three decades. The organization is expanding its investment in artificial intelligence and cloud technologies to strengthen its services and improve operational efficiency.About the OpportunityThe AI Platform Support Engineer supports the production AI platform, which leverages large language models, retrieval-augmented generation, and embedding-based search to improve auditor and operational workflows. Reporting to the AI Lead, this role helps maintain and enhance the AI Assistant Platform infrastructure while translating business needs into practical AI-driven improvements.Responsibilities• Support the daily operation and maintenance of the AI Assistant Platform, including platform health monitoring and issue triage.• Build and maintain retrieval-augmented generation (RAG) pipelines.• Configure and refine large language model agent behavior and prompt engineering.• Assist with data pipeline activities, including metadata extraction and semantic indexing.• Coordinate AI feature testing, gather auditor feedback, and track issue resolution.• Support document extraction workflows by validating and expanding template coverage.• Evaluate emerging AI tools and capabilities and provide recommendations.• Maintain technical documentation for AI platform components and workflows.• Assist with cloud storage, data source management, and cost optimization.• Support integrations between the AI platform and CRM systems.• Collaborate with cross-functional teams to translate business needs into AI platform enhancements.Requirements• 2–4 years of experience working with AI/ML tools, cloud platforms, data engineering, or software support.• Knowledge of AI and LLM concepts, including prompt engineering and retrieval-augmented generation.• Experience building or operating RAG or embedding-based systems.• Hands-on experience with at least one major cloud platform, such as AWS, Azure, or GCP.• Proficiency in Python or JavaScript.• Experience working with SQL databases.• Familiarity with AI-assisted development tools and work-tracking platforms.• Strong communication and technical documentation skills.• Self-motivated with demonstrated curiosity about emerging AI technologies.• Strong attention to detail when working with data pipelines and testing workflows.• Exposure to document extraction or OCR technologies is a plus.• Bachelor’s degree preferred but not required.Pay Range and Compensation Package• Competitive base salary with a starting target range of $65,000 to $80,000 per year, plus a performance-based bonus.Benefits & Perks• Health, dental, and vision insurance.• 401(k) plan with company match.• Generous paid time off.• Eleven paid holidays.
Equal Opportunity Statement: Our client is an equal opportunity employer. They celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, or national origin.
Note:RemoteHunter is not the Employer of Record (EOR) for this role. Our purpose in this opportunity is to connect exceptional candidates with leading employers. We help job seekers worldwide discover roles that match their goals and guide them to complete their full application directly through the hiring company’s career page or ATS. | full_time | remote | true | Associate | 25 | 2026-08-16T20:50:18.000Z | 2026-09-15T20:50:18.000Z | false | — | — | — | — | — | — | — | — | — | United States | RemoteHunter | 107496425 | remotehunter | 2 | 95,019 | |||||||
8 | 4443329222 | Machine Learning Engineer (LLM inference) | MLE (LLM inference)
About USGMI Cloud is a fast-growing AI infrastructure company backed by Headline VC and one of only six cloud providers worldwide to earn NVIDIA’s prestigious Reference Platform Cloud Partner designation . We operate 8 of our own GPU clusters across the U.S. and Asia, delivering a full spectrum of services from GPU compute service to AI model inference API solutions. As an NVIDIA Reference Platform Cloud Partner, our infrastructure meets the highest standards for performance, security, and scalability in AI deployments. We empower AI startups and enterprises to “build AI without limits,” providing everything they need to prototype, train, and deploy AI models quickly and reliably. About this role We are hiring a Machine Learning Engineer, LLM Optimization to build a world-leading inference optimization team and make GMI Cloud the industry benchmark for LLM serving performance.This role is for engineers who want to work at the frontier of AI systems. You will drive the research, validation, and productionization of the most advanced inference optimization techniques, and turn them into real competitive advantage across GMI’s inference platform.Our goal is to make GMI the company that leads the industry in how fast we discover, evaluate, combine, and operationalize the best optimization strategies for real customer workloads. That means not only adopting the latest advances, but also defining best practices, developing our own optimization methodologies, and building the internal framework that keeps GMI ahead of the curve.You will focus on B200-first optimization, with support for H200 evolution, across core domains including quantization, speculative decoding, KV cache and memory management, prefill/decode disaggregation, and system-level inference optimization. You will work closely with platform and infrastructure teams to transform cutting-edge ideas into measurable gains in latency, throughput, cost efficiency, and production scalability.Key ResponsibilitiesDrive frontier research and engineering in LLM inference optimization, building GMI’s industry-leading capabilities in performance, efficiency, and scalability.Develop next-generation optimization strategies for large-scale LLM serving across model execution, runtime systems, and production inference platforms.Advance state-of-the-art techniques in quantization and precision optimization to improve throughput, latency, memory efficiency, and cost-performance across modern GPU systems.Push the frontier of speculative decoding and related acceleration methods, including both systems and model-level approaches for faster generation.Lead innovation in KV cache and memory optimization, improving long-context serving efficiency, memory utilization, and multi-tenant performance.Develop advanced architectures for prefill/decode disaggregation and other distributed inference optimization strategies for large-scale production environments.Drive system-level optimization across scheduling, batching, routing, gateway orchestration, adapter serving, and end-to-end inference efficiency.Build scalable optimization frameworks, performance methodologies, and engineering practices that allow GMI to stay ahead of the industry as models, hardware, and serving patterns evolve.Turn cutting-edge optimization ideas into production-ready capabilities that improve real-world customer workloads across latency, throughput, quality, and cost.Collaborate closely with platform, infrastructure, and product teams to make inference optimization a core technical advantage of GMI Cloud.Required SkillsStrong hands-on experience with LLM inference systems and performance optimization.Solid understanding of inference metrics and tradeoffs, including TTFT, ITL, throughput, goodput, tail latency, GPU utilization, memory efficiency, and quality/cost tradeoffs.Experience with one or more modern serving stacks such as SGLang, vLLM, TensorRT-LLM, Triton, or similar systems.Deep familiarity with GPU-based inference, model serving architecture, and production bottlenecks around compute, memory bandwidth, KV-cache behavior, and scheduling.Strong experimentation skills: able to design benchmarks, interpret results, debug regressions, and produce actionable conclusions rather than isolated microbenchmark wins.Comfortable working across research-style validation and production engineering, with a bias toward measurable impact in real customer scenarios.Strong coding and systems skills in Python, with practical experience in profiling, observability, and performance debugging.Clear communication skills and the ability to explain technical tradeoffs to both engineers and cross-functional stakeholders.
Preferred Qualifications1+ years of hands-on experience in LLM inference optimization, ML systems optimization, or closely related areas.Experience working on optimization for large-scale model serving, such as latency reduction, throughput improvement, memory efficiency, or cost-performance tuning.Familiarity with one or more major areas of inference optimization, including quantization, speculative decoding, KV cache optimization, prefill/decode disaggregation, or system-level serving optimization.Experience with modern LLM serving stacks, GPU inference systems, or production ML infrastructure is a strong plus. | full_time | hybrid | false | Entry level | 200 | 2026-08-17T01:57:40.000Z | 2026-09-19T07:49:42.000Z | true | — | — | — | — | LinkedIn | Peggy Zhou | Mountain View | California | United States | GMI Cloud | 89671544 | gmi-cloud-ai | 137 | 39,172 | ||||||||
9 | 4455206146 | Founding AI Engineer | About The Role
A fast-growing, AI-first consumer social platform is hiring a Founding AI Engineer to join its core team in San Francisco. The company uses AI agents to facilitate real-world, in-person social connections — organizing curated small-group hangouts for young people, with a growing presence across university campuses in the US. The platform operates entirely through AI: matching, logistics, reminders, follow-ups, and continuous learning, all without a traditional app.
As a founding engineer, you will own two mission-critical pillars of the product and have an outsized impact on the direction of the technology.
What You'll Do
AI Matching & Social Graphs
Build a new type of social graph grounded in real-world interaction dataMatch people into highly compatible groups and surface patterns that drive meaningful friendshipsWork with millions of data points using collaborative filtering and graph intelligenceDevelop and iterate on embeddings, ranking systems, and large-scale personalizationApply reinforcement learning informed by real-world outcomes
Communication Workflows & AI Agent Experience
Design and build AI agent experiences across channels where users already are (e.g. Instagram, iMessage, WhatsApp, TikTok)Orchestrate multi-channel agent workflows connecting APIs, third-party services, and the database layerBuild scalable infrastructure to support complex, multi-step AI workflowsOwn human-AI interaction design for a seamless, natural user experience
What We're Looking For
Required:
4–10+ years of experience building AI/ML systems in productionComfort working across the full stack — from data pipelines to user-facing agent interactionsFamiliarity with multi-channel messaging APIs and agent frameworksFounding mindset: you ship fast, own problems end-to-end, and thrive in ambiguity
Nice to Have:
Experience with recommendation systems, social graphs, or large-scale personalization
Compensation & Benefits
Salary: $150,000 – $200,000 USD annuallyEquity: Meaningful founding-level equityEarly-stage opportunity with significant upside in a growing consumer tech company
Note: Visa sponsorship is not available for this role.
Location
This is a full-time, on-site role based in San Francisco, California. The team works together in person.
| full_time | on_site | false | — | 25 | 2026-08-16T19:56:54.000Z | 2026-09-15T19:56:54.000Z | false | — | — | — | — | — | — | — | — | San Francisco | California | United States | Clera | 105863333 | getclera | — | 20 | 12,572 | |||||
10 | 4455237148 | PROTEGE AI: PRINCIPAL SOFTWARE ENGINEER FULL STACK | FeaturesPricing✨
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PROTEGE AI: PRINCIPAL SOFTWARE ENGINEER FULL STACK
Online Jobs Philippines - Work at Home Palo Alto, CA
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Your resume needs significant work for this role. A full rewrite is recommended before applying.
Job Description
Headquarters: Palo Alto, CA. USA URL: https://www.protegecounsel.ai What we're looking for We are hiring for an ambitious, skilled Full Stack Developer with expertise in Node.js (TypeScript) development, React.js, and a range of associated technologies. This developer will move from back-end code to front-end UX implementation with tasteful design and integrate our platform with 3rd party APIs like Microsoft Office or Jira. Responsibilities 📝 Help guide our overall strategy through designDevelop and maintain server-side features in Node.js, leveraging TypeScript for efficient codebase management.Utilize React.js for building dynamic and user-friendly front-end interfaces.Implement and maintain high-quality UX/UI design principles across applications.Utilize Prisma ORM for effective database management, ensuring data integrity and efficiency.Troubleshoot and debug issues promptly, providing effective resolutions to maintain application functionality.Write comprehensive tests to ensure code quality and reliability.Demonstrate strong problem-solving and debugging skills in a collaborative team environment.Translate product requirements into user-centric features, showcasing independent thinking and creativity.Collaborate effectively with team members, demonstrating excellent communication and teamwork skills. Nice-to-Have Skills Experience in creating and optimizing OpenAI prompts for AI-powered applications.Familiarity with Railway or Vercel for serverless deployment and scaling of applications.Prior experience working with American startups and tech companies, understanding the fast-moving culture.Past integration experience with Salesforce, Smartsheets, Jira, or Asana APIs.Experience with Microsoft Word add-on extension development. Qualifications 👨🎓 Bachelor's degree in Computer Science, Engineering, or related field.6+ years of software development experienceProven experience in Node.js (TypeScript) development and React.js for front-end.Strong understanding of UX/UI principles with a keen eye for design.Experience with Prisma ORM and database management.Excellent problem-solving and debugging skills.Strong teamwork and communication abilities. About Us 🧑🤝🧑 We specialize in developing AI tools tailored for lawyers within corporate environments, akin to Grammarly but designed specifically for legal marketing tasks. Our innovative tool seamlessly integrates with popular task management platforms such as Jira, streamlining the legal marketing review process. Marketers and legal professionals leverage our solutions directly within familiar environments like Google Docs and Microsoft Office to identify and address potential issues within marketing content, effectively mitigating risks for their organizations. Companies spend hundreds to thousands of legal man-hours of manual marketing review each month, which is expensive and slows down the enterprise. We’re founded by former product managers, marketing lawyers, and early startup engineers with backgrounds like AWS, Meta, Lyft, and Block. Why Join Now? 🚀 We are a venture-funded company, and we’ve raised a $4M seed round, so you’ll have job stability while working on cutting-edge applied AI products. Our customer waitlist is rapidly expanding, comprising reputable, often publicly traded clients. We offer competitive compensation packages, accompanied by regular performance evaluations leading to title promotions and increased responsibilities for high performers. Embracing remote work, we provide flexibility while also facilitating regular on-site engagements to foster team collaboration and connection. To apply: https://weworkremotely.com/remote-jobs/protege-ai-principal-software-engineer-full-stack
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Playground
Run this API
Advanced parameters are collapsed below.
Input Form
Fill in the fields below or .
0.1¢ per job
No row cap set
Output limits
live
curl -X POST https://api.mindcase.co/v1/data/linkedin/jobs/run \
-H "Authorization: Bearer mk_live_YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"params": {
"jobTitles": "",
"locations": "",
"company": [],
"workplaceType": [],
"employmentType": [],
"experienceLevel": [],
"salary": [],
"postedLimit": "",
"sortBy": "",
"industryIds": []
}
}'Edit the form to update this live.
Overview
About LinkedIn Jobs API
LinkedIn Jobs extracts active job postings from a search — job title, company, location, the full job description, salary when listed, applicant counts, and a complete company profile (employee count, follower count, specialities, industries, and office locations).
Recruiters use LinkedIn Jobs to monitor talent demand in specific sectors. Sales teams use it to find companies expanding their engineering or marketing departments to identify high-intent leads.
Real-time dataSelf-servePay per resultZero setup time
Examples
Use cases
A few common ways teams put LinkedIn Jobs API to work — copy a prompt below to try it yourself.
Industry talent mapping
Extract a broad set of job listings to analyze hiring trends across specific regions or sectors.
Find 100 Data Scientist jobs in New York
Competitor hiring tracking
Monitor the open roles at specific organizations to understand their product roadmap and growth strategy.
100 open engineering roles at Microsoft, Google, and Meta
Targeted account research
Pull current openings for a single company to prepare for sales outreach or partnership discussions.
Active job postings for Stripe (latest 100)
Recent opening alerts
Filter for the most recent postings to ensure your data reflects the current job market.
Find 50 Product Manager jobs in London posted within the last week
Get started
Ready to fetch job listings?
Sign up to run live queries against LinkedIn Jobs API via chat, form, or API.
FAQ
Frequently asked questions
- Does this return the full job description text?
- Yes. LinkedIn Jobs returns the full job description for every posting, plus salary (when listed), applicant counts, and the complete company profile — not just the search-card metadata.
- How fresh is the data?
- The data is fetched live from the source on every run. LinkedIn Jobs does not use a database of cached results.
- Can I search for multiple job titles at once?
- Yes. Pass an array of job titles in a single call and LinkedIn Jobs fans out the requests in parallel. The response is one consolidated table with a column noting which search each row came from.
- How do I integrate with Zapier, n8n, Google Sheets, or Airtable?
- LinkedIn Jobs returns clean JSON and CSV output. Both formats work with every workflow tool. Pipe them in via a webhook step, file download, or by hitting the REST endpoint on a schedule. Same for Slack notifications, Notion databases, and other downstream systems.
- What fields are returned?
- Per job: job URL, title, full description, employment type, workplace type (remote/hybrid/on-site), applicants, posted & expiry dates, a unified apply URL plus an Easy Apply flag, industries, salary (min/max/currency/period when listed), the company's ATS, benefits, the recruiter (name + profile), job location (city/state/country), and the company (name, URL, website).
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