Applied AI, LLM and generative AI engineering roles from companies building AI products — listed from their own career pages.
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Applied AI, LLM and generative AI engineering roles. Updated daily.
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Posted 8 months ago
We operate drone delivery for medical supplies in rural Ghana and Kenya. Our fleet of 15 drones makes 80+ deliveries per day. Currently routes are pla...
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Posted 8 months ago
We're a mid-size mobile game studio. Our game is an open-world RPG where we want procedurally generated quests and dungeons that feel authored — not o...
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Posted 8 months ago
We make vibration, acoustic, and thermal sensors for industrial environments. Inference currently goes to cloud. We want it on the sensor. Need someo...
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Career context
An AI engineer builds software around models: LLM-powered features, retrieval pipelines, agents, evaluation harnesses and the infrastructure that keeps them fast and affordable. The role sits between machine learning research and product engineering — most AI engineers write production code every day and rarely train models from scratch.
Companies hiring AI engineers want proof that you have shipped. A deployed feature with real users, an evaluation suite that caught regressions, a latency or cost number you improved — these say more than a list of frameworks. The roles below come from AI labs, AI-native startups and product companies adding AI to existing software.
Why RemoteAI
Every company on RemoteAI is actively hiring AI talent. No noise, no ghost listings, no recruiter markups.
Every role on RemoteAI is an AI specialist position. No noise, no irrelevant listings from unrelated industries.
No recruiter in the middle. Your application goes straight to the company making the decision. Faster responses, no markups.
Creating a profile, applying, and getting hired costs nothing. No placement fee, no revenue percentage, no exclusive contracts.
Every listing shows compensation range before you apply. No wasted interviews with companies outside your range.
Every role is remote. Some require timezone overlap — the listing will specify. Filter by region if that matters to you.
Track all your applications, messages, and interviews in one place. No more losing track of where you applied.
Step by step
AI engineering is one of the best-paid remote engineering tracks. Senior roles at AI-native companies often publish ranges well above general software engineering; each listing shows the range the employer published.
Link a deployed AI feature, a repo with an evaluation suite, or a write-up with real numbers (latency, cost, accuracy). Shipped work beats certificates.
Which models, which retrieval approach, which serving setup — and why. Explaining trade-offs is what interviewers test.
Each listing links to the company's official application. Tailor the first lines of your profile to the role's product.
Profile guide
Not all profiles get responses. These are the signals that separate candidates who get interviews from those who don't.
Golden datasets, regression tests for prompts, LLM-as-judge with human checks. Teams that ship AI reliably all have this; few candidates show it.
Chunking, embeddings, hybrid search, reranking. RAG quality decides most LLM products.
Function calling, multi-step agents, guardrails and recovery when a tool fails.
Caching, batching, model routing, smaller models where they are enough. Every AI product team watches its inference bill.
Observability, rate limits, fallbacks, CI. AI engineers are software engineers first.
Knowing when AI is the wrong answer is as valuable as knowing how to build it.
Explore more roles
Production ML, deep learning & AI infrastructure
RAG pipelines, agentic systems & generative AI
Analytics, forecasting & applied ML
ML infrastructure, model serving & CI/CD
Language models, transformers & text AI
AI-native product strategy & roadmap
Image AI, object detection & edge deployment
AI agents, n8n & workflow automation
LLM evaluation, RAG & prompt optimization
AI tutoring, RLHF & red teaming
Labeling, rating, evaluation & transcription
Pipelines, ETL & data platforms
Ship AI solutions with customers
Alignment, evaluations & responsible AI
Junior roles & AI internships
Contract & freelance AI projects
FAQ
Everything you need to know before applying for your first AI engineer role.
Machine learning engineers usually train, tune and deploy models. AI engineers mostly build products on top of existing models — LLM features, retrieval, agents, evaluation and serving. The titles overlap, so read each listing: it says whether the job is closer to model training or to product engineering.
No. Most AI engineer roles ask for strong software engineering and hands-on experience building with models. Research-heavy roles at AI labs are the exception and say so in the listing.
Python, LLM APIs, retrieval (RAG) and vector search, evaluation, agents and tool use, plus general backend and cloud skills. Experience shipping to real users is the most common requirement.
They are listed as remote by the employer. Some limit remote work to certain countries or time zones — the location on each listing says where the company can hire.
Yes. Creating a profile, browsing and applying are free, and RemoteAI takes no cut of your salary.
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