ML systems engineer

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.

Responsibilities

• 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

Requirements

• 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

Location

Remote (IST ±4)

Type

Full-time

Package

Competitive equity