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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
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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
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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
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Nice to have

• —Experience with LLM serving, agent orchestration, or retrieval systems
• —Published evaluation or benchmarking work
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Not quite your role? See the other three openings.

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ML systems engineer

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Why this role

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