---
title: 'Senior Applied ML Engineer | Kimchi (Model Router) at Cast AI'
canonical: 'https://feeny.ai/job/senior-applied-ml-engineer-kimchi-model-router-cast-ai-austria-france-germany-561z085x507g'
type: 'job'
last_seen: '2026-10-17'
---

# Senior Applied ML Engineer | Kimchi (Model Router) at Cast AI

- **Company:** Cast AI
- **Location:** Austria / France / Germany / Italy / Netherlands / Poland / Spain / United Kingdom
- **Posted:** 2026-10-15
- **Last confirmed live:** 2026-10-17
- **Apply:** https://cast.ai/careers/apply/?gh_jid=4451280009

## Job description

Why Kimchi?

Great 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.

The Auto Router

Our 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.

Made 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.

## About the role

Every request gets a decision: which model serves it.

This 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.

The problem: routing 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.

Stack: Python; PyTorch; SQL at scale (ClickHouse); OpenAI- and Anthropic-compatible LLM APIs; benchmark and rollout harnesses (RouterBench-class); GitLab CI.

## Requirements

- You'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.
- Strong Python and SQL at scale: you can pull, clean, and interrogate millions of request-level records without waiting on anyone.
- You know the difference between a model that got better and an eval that got easier.
- You have real opinions about what LLM benchmarks measure, agentic harnesses included.
- You know enough serving reality (TTFT, decode speed, batching, prefix caching, concurrency) to model the objective honestly.
- You instrument before you optimize, error bars and all.
- A wide mandate excites you more than it unsettles you.

Nice to haves

- You've worked on LLM routing specifically: RouterBench-class evals, cost/quality/latency Pareto fronts.
- Calibration, abstention, or conformal methods in production decision systems.
- You've run online A/B loops with drift monitoring on a model that touches real money.

## Responsibilities

- Own the routing model end to end: features, objective, training, calibration, and release cadence. Iterate it like a product, not a research artifact.
- Build 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.
- Turn 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.
- Model 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.
- Run 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.
- Push 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.
- Partner 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.

What’s in it for you?

- Competitive salary (depending on the level of experience).
- Enjoy a flexible, remote-first global environment.
- Collaborate with a global team of cloud experts and innovators, passionate about pushing the boundaries of Kubernetes technology
- Equity options.
- Get quick feedback with a fast-paced workflow. Most feature projects are completed in 1 to 4 weeks.
- Spend 10% of your work time on personal projects or self-improvement.
- Learning budget for professional and personal development - including access to international conferences and courses that elevate your skills.
- Annual hackathon to spark new ideas and strengthen team bonds.
- Team-building budget and company events to connect with your colleagues.
- Equipment budget to ensure you have everything you need.
- Extra days off to help maintain a healthy work-life balance.

Hiring process

- Screening call with Recruiter
- Hiring Manager interview
- Technical interview (system design)
- Live coding
- Culture Check interview with an executive

As 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. Please note that Cast AI does not provide any form of visa sponsorship/work permit.

#LI-Remote

## About Cast AI

## Company Overview
- **One-liner**: Cast AI provides an Application Performance Automation (APA) platform that continuously optimizes Kubernetes infrastructure for cost, performance, and reliability with minimal manual intervention.
- **Entity Type**: Private (Series C; total funding ~$185.8M–$199.9M per conflicting reports)
- **Headquarters**: Miami, Florida, United States
- **Founded**: 2019
- **Founders**: Leon Kuperman (CEO), Laurent Gil (President), Yuri Frayman (CTO)

## Core Business
- **Primary industry**: Kubernetes optimization, cloud cost management, and application performance automation.
- **Target customers**: Platform, SRE, and FinOps teams at B2B companies running Kubernetes (SMB to enterprise).
- **Mission/purpose statement**: “The cloud as promised: fast, reliable and cost-efficient.” [cast.ai/about-us](https://cast.ai/about-us/)

## Products & Services
- **Cast AI APA Platform**: A SaaS platform that ingests real-time workload, cost, and SLO signals to autonomously rightsize pods, scale nodes, optimize GPU and Spot instances, and self-heal operational issues. Supports AWS EKS, Azure AKS, Google GKE, and on-prem clusters. Includes cost and performance dashboards, agentic runbooks, and workload-aware predictive models. [cast.ai](https://cast.ai/)

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed.
- **Key Metric**: Annual Revenue reported as ~$60M (LinkedIn); Total Funding ~$185.8M (LinkedIn) or ~$199.9M (CB Insights – conflicting reports).
- **Notable Investors/Partners**: Specific investors are not named in available sources; funding rounds include Series B ($35M, Nov 2023, 3 investors), a venture round ($20M, Mar 2023), and Series C ($108M, Apr 2025, 8 investors). [linkedin.com/company/cast-ai](https://www.linkedin.com/company/cast-ai) | [cbinsights.com/company/cast-ai](https://www.cbinsights.com/company/cast-ai)
- **Growth Signals**: 2100+ customers globally; 40% cloud waste reduction claimed; 270 employees (+19.4% YoY); ranked #1 out of 223 solutions in its category; offices in Miami, New York, Vilnius, London, Tel Aviv, Bengaluru, Dallas. [cast.ai/about-us](https://cast.ai/about-us/) | [linkedin.com/company/cast-ai](https://www.linkedin.com/company/cast-ai)

## Competitive Advantages
- **Predictive engine trained on massive dataset** from thousands of clusters and millions of workloads – moves beyond rule-based automation to workload-aware decisions.
- **App-aware reliability**: Predicts spot instance interruptions up to 30 minutes before they occur, enabling graceful workload migration.
- **Agentic runbooks** that self-heal drift, image issues, policy violations while requiring human approval for changes.
- **Works in read-only mode initially** – no infrastructure changes required to start, lowering adoption risk.

## Strategic Focus
- Expanding automation to GPU/AI workload optimization alongside traditional Kubernetes rightsizing.
- Deepening integrations with cloud providers (AWS, Azure, Google) and extending agentic capabilities.
- Scaling global presence with remote-first teams and new offices in key tech hubs.

## Why Work Here
- **Remote-first** culture with flexible hours and fully supported remote work; offices available in multiple countries. [cast.ai/careers](https://cast.ai/careers/)
- **Benefits**: Stock options from day one, annual hackathon, home office allowance, learning & development budget, comprehensive health insurance, bonus days off, company events.
- **Engineering culture**: Values “practice customer obsession,” “develop and hire the best,” “lead by ownership,” and “expect and advocate change.” Emphasis on innovation and fast learning.
- **Glassdoor style**: Employer rating 4.3/5 (57 reviews); culture 4.0, compensation 4.2, career 4.2. [linkedin.com/company/cast-ai](https://www.linkedin.com/company/cast-ai)

## Sources
1. [cast.ai/about-us](https://cast.ai/about-us/)
2. [cast.ai](https://cast.ai/)
3. [cast.ai/careers](https://cast.ai/careers/)
4. [linkedin.com/company/cast-ai](https://www.linkedin.com/company/cast-ai)
5. [cbinsights.com/company/cast-ai](https://www.cbinsights.com/company/cast-ai)

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