Чем предстоит заниматься
You'll set technical direction for how we train and evaluate models at scale, stay close enough to the code to debug alongside your team, and work daily with researchers to turn tradeoffs in latency, quality, and cost into infrastructure that actually gets built. We're hiring across a range of scope for this role, depending on experience and the size of problem you're ready to own
EXAMPLE PROJECTS INCLUDE
Building the rollout infrastructure that lets researchers run RL experiments at scale without fighting the plumbing
Designing eval pipelines that catch regressions before they ship, and give researchers fast, trustworthy signal on whether a change actually helped
Owning the environments in which models are trained and tested: sandboxed, reproducible, and fast enough that iteration speed isn't the bottleneck
Bringing rigor to how the team measures quality and progress, in places where "did it ship" isn't the same as "did it work?"
Partnering with research to translate model-level tradeoffs (latency, quality, cost) into concrete infrastructure decisions
Hiring and growing the team: sourcing, interviewing, and closing exceptional infrastructure engineers, while developing your engineers through coaching, mentorship, and high-leverage project assignments