Skip to main content

Documentation Index

Fetch the complete documentation index at: https://resources.devweekends.com/llms.txt

Use this file to discover all available pages before exploring further.

The AI job market moves differently from traditional software engineering. Many AI companies are remote-first by design (training runs happen in the cloud, not in an office), and the demand for engineers who can build production AI systems far outstrips supply. This creates real opportunities, but you need to know where to look and how to position yourself. Each platform serves a different segment of the market. Use multiple sources simultaneously:
  • Wellfound (formerly AngelList) — Best for funded startups (Series A through C). Filter by “Remote” and “AI/ML” tags. Many YC-backed AI companies post here first.
  • YC Work at a Startup (workatastartup.com) — Direct applications to Y Combinator portfolio companies. These tend to be fast-moving, high-equity roles. Filter by “AI” and “Remote” for the best matches.
  • RemoteOK (remoteok.com) — Aggregates remote roles across industries. Use keyword filters like “LLM,” “machine learning,” or “AI engineer” to find relevant listings.
  • Hacker News “Who’s Hiring” threads — Posted on the 1st of every month. Search for “remote” + “AI” or “ML.” These are often direct from hiring managers, so your application goes straight to a decision-maker rather than through an ATS.
  • Company career pages directly — Companies like Anthropic, OpenAI, Cohere, Hugging Face, Mistral, Stability AI, Runway, and Replicate post roles on their own sites before (or instead of) job boards. Bookmark their career pages and check weekly.
  • LinkedIn with smart filters — Search for “AI Engineer” or “ML Engineer,” filter by “Remote,” and set up job alerts. Reach out directly to hiring managers or team leads alongside your formal application.

​
How to Stand Out

AI hiring managers see hundreds of resumes listing “experience with ChatGPT.” Here is what actually differentiates candidates:
  • Showcase production-grade projects, not just experiments. A RAG pipeline that handles 10K queries/day with latency monitoring and fallback logic impresses far more than a Jupyter notebook calling the OpenAI API. Include architecture diagrams, latency numbers, and cost breakdowns in your portfolio.
  • Demonstrate infrastructure awareness. Vector databases (Pinecone, Weaviate, Qdrant), orchestration frameworks (LangChain, LlamaIndex, Haystack), model serving (vLLM, TGI, Triton), and observability tools (LangSmith, Phoenix) — knowing the tooling ecosystem signals you can build systems, not just prototypes.
  • Quantify outcomes. “Built a document Q&A system” is weak. “Built a RAG-based Q&A system that reduced customer support tickets by 35% and handles 500 concurrent users with p95 latency under 2 seconds” is strong.
  • Add a dedicated AI section to your resume listing: models you have worked with (GPT-4, Claude, Llama, Mistral), frameworks (LangChain, Haystack, DSPy), infrastructure (vector DBs, model serving, GPU provisioning), and evaluation methodologies you have used.
  • Contribute to open-source AI projects. Even small contributions to projects like LangChain, Hugging Face Transformers, or vLLM demonstrate hands-on familiarity and show up on your GitHub profile for hiring managers who check.

​
Understanding the AI Role Landscape

Not all “AI roles” are the same. Know which you are targeting:
  • AI/ML Engineer — Builds and deploys models and pipelines. Needs strong Python, MLOps, and systems design skills.
  • AI Application Engineer — Builds products on top of foundation models (RAG, agents, chatbots). Needs strong software engineering plus LLM API experience.
  • ML Research Engineer — Implements and experiments with novel architectures. Needs deep math/stats background plus PyTorch proficiency.
  • AI Infrastructure Engineer — Builds the platform (GPU clusters, model serving, data pipelines). Needs strong distributed systems and DevOps skills.
Apply to 3-5 companies per week rather than mass-applying to 50 at once. Tailoring your resume and writing a short note to the hiring manager for each application dramatically outperforms volume-based approaches. Quality of applications beats quantity in the AI space, where teams are small and hiring is selective.
We are compiling a maintained list of remote-friendly AI companies with details on their tech stacks, interview processes, and team sizes. Contribute via PR if you have first-hand experience.