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We spent three years building a no-code workflow automation tool. It didn't work. Not because the technology was wrong, but because we could never fin...
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We got into YC six months ago. We launched three months ago. We have 11,000 users and $0 in VC funding beyond our YC check, which means every decision...
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We are a three-year-old fintech building credit risk tools for emerging market lenders. Our core product uses ML to generate creditworthiness scores f...
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We build an AI research assistant for universities and research institutions. The system ingests thousands of academic PDFs, extracts and indexes doma...
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We want to be honest about what it's like to be junior at our company, because we think it matters when you're deciding where to grow. You'll write co...
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50 data scientists. Every one deploys differently. Some use a shared EC2 instance. Some maintain their own containers. There is no model registry that...
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We want to say something clearly at the start of this posting: we are actively looking for early-career candidates, including people who are making a...
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Our finance team spends every Monday doing the same manual reconciliation process. Our HR team reviews CVs one by one. Our ops team re-enters data bet...
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Context: we serve 1.2 million users. Our AI features handle 300,000 requests per day. Our P95 latency for LLM calls is currently 4.2 seconds. Our targ...
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We are building the next generation of our product on agentic AI — workflows where multiple LLM agents collaborate to complete complex tasks autonomou...
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Career context
AI engineers turn models into products
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
Built for AI specialists.
Not general job boards.
Every company on RemoteAI is actively hiring AI talent. No noise, no ghost listings, no recruiter markups.
AI-only job listings
Every role on RemoteAI is an AI specialist position. No noise, no irrelevant listings from unrelated industries.
Direct access to hiring teams
No recruiter in the middle. Your application goes straight to the company making the decision. Faster responses, no markups.
Always free for candidates
Creating a profile, applying, and getting hired costs nothing. No placement fee, no revenue percentage, no exclusive contracts.
Salary shown upfront
Every listing shows compensation range before you apply. No wasted interviews with companies outside your range.
Fully remote positions
Every role is remote. Some require timezone overlap — the listing will specify. Filter by region if that matters to you.
Full application tracking
Track all your applications, messages, and interviews in one place. No more losing track of where you applied.
Step by step
How to find an AI engineer job on RemoteAI
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.
Show something you shipped
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.
Name your stack and choices
Which models, which retrieval approach, which serving setup — and why. Explaining trade-offs is what interviewers test.
Apply on the employer's page
Each listing links to the company's official application. Tailor the first lines of your profile to the role's product.
Profile guide
How to stand out as an AI engineer
Not all profiles get responses. These are the signals that separate candidates who get interviews from those who don't.
Evaluation discipline
Golden datasets, regression tests for prompts, LLM-as-judge with human checks. Teams that ship AI reliably all have this; few candidates show it.
Retrieval and context
Chunking, embeddings, hybrid search, reranking. RAG quality decides most LLM products.
Agents and tool use
Function calling, multi-step agents, guardrails and recovery when a tool fails.
Cost and latency
Caching, batching, model routing, smaller models where they are enough. Every AI product team watches its inference bill.
Production engineering
Observability, rate limits, fallbacks, CI. AI engineers are software engineers first.
Product sense
Knowing when AI is the wrong answer is as valuable as knowing how to build it.
Explore more roles
Every AI specialty. One platform.
ML Engineer jobs
Production ML, deep learning & AI infrastructure
LLM Engineer jobs
RAG pipelines, agentic systems & generative AI
Data Scientist jobs
Analytics, forecasting & applied ML
MLOps Engineer jobs
ML infrastructure, model serving & CI/CD
NLP Engineer jobs
Language models, transformers & text AI
AI Product Manager jobs
AI-native product strategy & roadmap
Computer Vision Engineer jobs
Image AI, object detection & edge deployment
AI Automation Specialist jobs
AI agents, n8n & workflow automation
Prompt Engineer jobs
LLM evaluation, RAG & prompt optimization
AI Trainer jobs
AI tutoring, RLHF & red teaming
Data Annotation jobs
Labeling, rating, evaluation & transcription
Data Engineer jobs
Pipelines, ETL & data platforms
Forward Deployed Engineer jobs
Ship AI solutions with customers
AI Safety & Governance jobs
Alignment, evaluations & responsible AI
Entry-Level AI Jobs jobs
Junior roles & AI internships
Freelance AI Jobs jobs
Contract & freelance AI projects
FAQ
Questions about remote AI engineer jobs
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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