The role is simple to state and hard to do well: make that optimization save more tokens without ever costing output quality or breaking a provider's cache. It's a research-heavy engineering role – you form theories from real traffic and ship the ones that prove out on live customer requests.
Why this role
LLM optimization is a young field with no settled playbook, so the optimizations you invent here are genuinely new – and they ship into production, not a paper. And they matter: real enterprises run enormous volumes of LLM traffic and feel every wasted token, so the work you do lands on live customer requests and solves a problem they actually have. It's about as close to the frontier, and as close to the product, as engineering gets.
Requirements:
Strong engineering fundamentals, and the judgment to tell when something is actually correct, fast and safe rather than just green in CI.
A real research instinct. You're fine with ambiguity, you form theories and drop them when the data says no, and you'd rather be right than clever.
You already build AI-native. Coding agents and LLM pipelines are part of your day, and you've used them to do work that used to take a team.
You know LLMs at a low level: tokenization, context windows, how caching actually behaves, streaming, tool calls, and how the various providers bill.
Staff-level range. You've carried big, vague pieces of work on your own and shaped how a team builds.
Strong engineering fundamentals, and the judgment to tell when something is actually correct, fast and safe rather than just green in CI.
A real research instinct. You're fine with ambiguity, you form theories and drop them when the data says no, and you'd rather be right than clever.
You already build AI-native. Coding agents and LLM pipelines are part of your day, and you've used them to do work that used to take a team.
You know LLMs at a low level: tokenization, context windows, how caching actually behaves, streaming, tool calls, and how the various providers bill.
Staff-level range. You've carried big, vague pieces of work on your own and shaped how a team builds.
This position is open to all candidates.
