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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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M&A due diligence requires reviewing thousands of contracts, financial statements, and regulatory filings under extreme time pressure. A typical deal...
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API documentation is either auto-generated and useless or hand-written and outdated. Our platform finds the middle ground — using AI to generate API r...
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PyTorch, Airflow, and Kubernetes are the core of what we build. If you've ever been the person who gets paged when a model serving endpoint goes down...
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As a fully async team spread across 9 time zones, we believe great engineers don't need to be in the same room — or even the same continent — to build...
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Have you ever wanted to build AI systems that actually go to production — not just demos? We ship LLM-powered features to 8,000 paying users every mon...
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We have 12 ML models that work beautifully in notebooks. Getting them into production is another story. Our data science team is brilliant at modeling...
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The team is 11 engineers, the product is live, and our customers are asking for more. We build an AI-powered research assistant for investment analyst...
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Our ML platform serves 8 internal teams and supports over 40 models in production. It's built on GCP with Kubernetes, MLflow, and Airflow — and it wor...
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Our product indexes and searches 50 million academic papers for research institutions worldwide. We've built a solid keyword search engine, but our cl...
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AI API platforms get abused in creative ways — one client sends 10,000 requests in a burst, another distributes the same volume across 100 API keys, a...
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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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