Senior applied ml engineer | kimchi (model router)
Pubblicato il 24-10-2026 - Cast Ai in Italia
H3Why Kimchi? /h3 pGreat models are raw ingredients. Kimchi is what happens when you ferment them with good engineering. We started inside CAST AI helping companies run LLMs on their own Kubernetes clusters; today we run our own coding harness on any model you pick, and the router deciding which model answers each request is ours too. /p h3The Auto Router /h3 pOur promise is one enforcement layer for your AI: budgets, spend attribution, and model routing, applied before a request ever hits a model. The Auto Router makes the call: for every request, which model serves it. Eight open-source models whose input prices span more than 20×, cached input up to 50× cheaper than fresh, frontier providers on customers' own keys, and a coding agent that fires hundreds of requests before its task is done. Frontier-level task success at a fraction of the token cost: that sentence is the product, and it stays true only if the model inside the router keeps getting better. That model is yours. /p pMade by Cast AI – a Miami-headquartered, globally distributed team across 34 countries that hit unicorn status in January 2026 with a $50B+ backed investment pushing its valuation past $1 billion. /p h3About The Role /h3 pEvery request gets a decision: which model serves it. /p pThis is an applied-ML seat, not an infrastructure one. Platform engineers own the proxy, the streaming path, and the fleet. You own the model inside them: the training data, the objective, the evaluation, and the iteration loop that ships a better router every cycle. /p h3The problem: /h3 prouting is a prediction problem wearing a product's clothes. For every request you predict the quality each candidate model would deliver, what it would cost, and how fast it would decode, and you have to be right enough, in aggregate, that the savings are real and the quality never dips below frontier. The data is messy: rollout outcomes from benchmark harnesses, per-request records from the live fleet, override signals, and token streams whose speed depends on the serving concurrency at the moment. Speed itself is a correctness variable: on deadline-bound agentic work, a model that decodes three times slower doesn't do the same work slower, it does less work. That's why naive cost-per-token objectives quietly optimize for the wrong thing. And the bar for evidence is high:
a router change ships only after production-parity evaluation with error bars, then gets held to its numbers in live traffic. /p h3Stack: /h3 pPython; PyTorch; SQL at scale (ClickHouse); OpenAI- and Anthropic-compatible LLM APIs; benchmark and rollout harnesses (RouterBench-class); GitLab CI. /p h3Requirements /h3 ul liYou've spent 5+ years in applied ML or ML research, and you've trained, evaluated, and shipped models that make real-time decisions (ranking, recommendation, bandits, pricing, or LLM routing) and you can tell that story with receipts. /li liStrong Python and SQL at scale: you can pull, clean, and interrogate millions of request-level records without waiting on anyone. /li liYou know the difference between a model that got better and an eval that got easier. /li liYou have real opinions about what LLM benchmarks measure, agentic harnesses included. /li liYou know enough serving reality (TTFT, decode speed, batching, prefix caching, concurrency) to model the objective honestly. /li liYou instrument before you optimize, error bars and all. /li liA wide mandate excites you more than it unsettles you. /li /ul h3Nice to haves /h3 ul liYou've worked on LLM routing specifically: RouterBench-class evals, cost/quality/latency Pareto fronts. /li liCalibration, abstention, or conformal methods in production decision systems. /li liYou've run online A/B loops with drift monitoring on a model that touches real money. /li /ul h3Responsibilities /h3 ul liOwn the routing model end to end: features, objective, training, calibration, and release cadence. Iterate it like a product, not a research artifact. /li liBuild the evaluation that gates every change: production-parity benchmarks, router-vs-router comparisons, holdouts by task family, error bars on pass rates. Nothing ships on vibes. /li liTurn fleet data into training signal: per-request outcomes from production, benchmark rollouts,
and override feedback, with failure categorization (infra vs task) so poisoned labels never reach the policy. /li liModel the objective the user actually experiences: task success × token cost × wall-clock speed under the real concurrency regime, with cached-input economics in the picture. /li liRun the online loop: shadow, A/B, full rollout, drift monitoring, retrain. When production disagrees with your offline eval, you find out why before the next ship. /li liPush the frontier where it pays: calibrated decisions, abstention under uncertainty, speed-aware routing. We experiment on what a router should be, not just which model it picks today. /li liPartner with platform engineers on instrumentation: the features and labels recorded per request (decode speed first-class among them) and the experiments the data path must support. /li /ul h3What’s in it for you? /h3 ul liCompetitive salary (depending on the level of experience). /li liEnjoy a flexible, remote-first global environment. /li liCollaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology /li liEquity options. /li liGet quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks. /li liSpend 10% of your work time on personal projects or self-improvement. /li liLearning budget for professional and personal development - including access to international conferences and courses that elevate your skills. /li liAnnual hackathon to spark new ideas and strengthen team bonds. /li liTeam-building budget and company events to connect with your colleagues. /li liEquipment budget to ensure you have everything you need. /li liExtra days off to help maintain a healthy work-life balance. /li /ul h3Hiring process /h3 ul liScreening call with Recruiter /li liHiring Manager interview /li liTechnical interview (system design) /li liLive coding /li liCulture Check interview with an executive /li /ul pAs part of our standard hiring process, we would like to inform you that a background check may be conducted at the final stage of recruitment through our third-party provider, Checkr. /p pPlease note that Cast AI does not provide any form of visa sponsorship/work permit. /p #J-18808-Ljbffr