Senior Applied ML Engineer | Kimchi (Model Router)

Role overview

Develop and improve the model that selects which language model should answer each incoming request. This applied machine-learning role owns the routing model’s data, objective, evaluation, and release cycle, with the aim of balancing task success, response speed, and cost. Decisions are validated against production-like workloads and monitored after release.

Responsibilities

- Own the routing model lifecycle, from feature design and training through calibration and release. - Build evaluations that compare routing approaches across task types, using production-parity benchmarks, holdouts, and uncertainty estimates. - Turn request outcomes, benchmark runs, and user overrides into training data while separating infrastructure failures from task failures. - Define objectives that account for task success, token expense, response time, serving concurrency, and cached-input costs. - Run shadow deployments, A/B tests, broader rollouts, and drift monitoring; investigate gaps between offline evaluation and live results. - Work with platform engineers on request instrumentation, labels, and experiments, and explore speed-aware routing and decisions under uncertainty.

Requirements

- At least five years in applied machine learning or research, with experience training, evaluating, and shipping models for real-time decisions such as ranking, recommendations, pricing, bandits, or LLM routing. - Strong Python and SQL skills for extracting and analyzing large volumes of request-level data. - Sound evaluation judgment, including the ability to tell genuine model improvement from changes in benchmark difficulty. - Understanding of LLM benchmarks and agentic evaluation, as well as serving factors such as time to first token, decoding speed, batching, caching, and concurrency. - Habit of instrumenting systems and reporting uncertainty when measuring results.

Nice to have

Experience with LLM routing benchmarks and cost-quality-latency trade-offs, calibration or abstention methods in production, or online A/B testing with drift monitoring.

Benefits and work setup

The position is remote-first. Listed benefits include equity options, a learning budget, equipment support, extra days off, and time for personal development. Visa sponsorship is not available.