Machine Learning Systems Engineer

Home/Careers/ Careers Models ML systems engineer Make model serving boring: routing, fallback, evaluation, and guardrails that hold up when a client's traffic triples overnight. Team Models Location Remote (IST ±4) Type Full-time Package Competitive equity About The Role You'll sit between research output and production reality — taking models that work in a notebook and making them serve at 12ms p50 with a contract behind the number. Expect to spend as much time on evaluation harnesses and failure modes as on throughput. What you'll do ▸Build and tune the model-serving path: batching, caching, routing, and fallback▸Own the evaluation harness — offline benchmarks and online quality signals▸Design guardrails, and the observability to prove they're working▸Advise clients on build-vs-buy and model selection with evidence, not vibes What we're looking for ▸4+ years shipping ML systems to production, not just training them▸Strong Python, and comfort dropping into a lower-level language when latency demands it▸Hands-on with inference servers and the throughput/latency trade-offs they force▸You can explain a p99 regression to a non-specialist stakeholder Nice to have —Experience with LLM serving, agent orchestration, or retrieval systems—Published evaluation or benchmarking work Not quite your role? See the other three openings. Apply ML systems engineer No cover letter needed. Links and a few honest lines beat a formatted CV. Leave this empty Name Email Links Why this role