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Browse vetted remote FastAPI positions — model serving APIs, LLM backend engineering, and production inference systems at companies shipping real AI products.
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Posted 3 months ago
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Speculative decoding is one of the most impactful LLM serving optimizations of the past two years: use a small draft model to generate candidate token...
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We deploy AI models for 45 enterprise clients. Every deployment generates metrics: pre-deployment test results, canary analysis outcomes, rollout spee...
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Data contracts solve the 'my pipeline broke because upstream changed the schema' problem — but only if you can track what version of the contract each...
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School districts and corporate L&D teams that license our adaptive learning platform have one question above all others: did the learners actually lea...
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Our conversational AI platform powers customer service bots for 95 enterprise clients. Every bot has tens of thousands of conversations per week. How...
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Three months ago, a model in production was making predictions that degraded silently for 11 days before anyone noticed. The postmortem showed the roo...
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Content teams produce. Content rarely knows if it worked. Our platform serves 160 B2B content marketing teams, and the gap between 'we published 40 pi...
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We process 40,000 financial documents per month. Invoices, purchase orders, receipts — that pipeline runs smoothly. Contracts are the next frontier, a...
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Stack Overflow's 'how do I do X in Python' worked when codebases were small. When your codebase is 8 million lines of proprietary code, the question i...
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Our marketplace has 2.8 million active listings. 340,000 of them have product descriptions under 50 words. 180,000 have no category assignment. 95,000...
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Role guide
FastAPI has become the default framework for serving AI models and LLM applications in Python, largely because its async support and automatic validation fit naturally with inference workloads. Remote FastAPI jobs at AI companies usually mean building the API layer between a model and the rest of a product — handling streaming responses, request batching, and production reliability, not just wrapping a model in an endpoint.
What remote FastAPI jobs are posted on RemoteAI?
Mostly AI backend and model-serving roles — building the API layer for LLM products, ML inference endpoints, and internal tooling that connects models to the rest of a product. Most require production Python experience beyond basic CRUD APIs.
Why do AI companies prefer FastAPI over Flask or Django?
Native async support handles the I/O-bound nature of model inference and streaming LLM responses well, and built-in Pydantic validation reduces boilerplate for the structured request/response formats AI APIs typically need.
Do FastAPI jobs require ML experience, or just backend skills?
It varies. Some roles are pure backend engineering — you serve models built by others. Others expect you to understand model behavior well enough to design sensible APIs around it (batching, timeouts, streaming). Check individual listings for the split.
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