---
title: 'Senior ML Engineer | Kimchi (Inference Stack Optimization) at Cast AI'
canonical: 'https://feeny.ai/job/senior-ml-engineer-kimchi-inference-stack-optimization-cast-ai-austria-france-8pyy2hw5mzq4'
type: 'job'
last_seen: '2026-10-17'
---

# Senior ML Engineer | Kimchi (Inference Stack Optimization) 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=4451243009

## 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 we run the models underneath it ourselves.

Kimchi Inference

One API line and a customer's existing client just works: DeepSeek, GLM, Kimi, MiniMax, Nemotron served serverless from our own GPU fleet (OpenAI- and Anthropic-compatible, pay per token, spend visible per user, team, and key). When control matters, the same models run on the customer's own cluster instead. Behind that one line sits the machinery you'll own: SGLang and vLLM engines, B300 nodes, a shared cluster filesystem that holds every checkpoint exactly once, KV caches riding coding traffic with heavy prefix reuse, and the benchmark harness that gates every change we ship.

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

Throughput. Latency. Cold starts. Cost per token.

Move those four numbers in the right direction and two things happen: customers get faster, cheaper inference, and our margins improve. That's the entire thesis of this role. Every loader you tune, every scheduler decision you land, every kernel you stare at shows up in a customer's p99 and on our P&L. Your mandate is the inference stack's optimization, whatever it takes, and the technical direction of how we optimize it is yours to argue with data.

The problem: the checkpoints run 0.3-1.5 TB, and the "right" serving configuration depends on traffic shape, sequence-length distribution, batch dynamics, GPU SKU, memory bandwidth, and quantization tolerance, all variables that shift week to week. Most teams pick a configuration once, over-provision GPUs, and absorb the cost. We don't: we took Kimi-K3 (1.5 TB) from over an hour cold-to-serving to under twelve minutes with a shared filesystem, O_DIRECT weight loaders, and node-local JIT caches, and we're nowhere near done. Prefill/decode disaggregation and tiered KV caches are next.

Stack: Python; SGLang; vLLM; TensorRT-LLM; PyTorch; fastsafetensors; CUDA-adjacent tooling; Kubernetes; virtiofs shared filesystems; ClickHouse; OpenTelemetry; Prometheus; Grafana; GitLab CI.

## Requirements

- You've built real ML systems for 5+ years, and your portfolio shows depth in inference or training infrastructure, not just model training notebooks.
- Your Python is production-grade: services, not scripts.
- You've run vLLM, SGLang, or TensorRT-LLM for real, and you have a working mental model of why an engine performs the way it does on a given GPU.
- You've measured quantization regressions, not just compression ratios.
- Multi-GPU and multi-node setups hold no fear: tensor and expert parallelism, collective communication, and their practical failure modes.
- You know where the bytes live (page caches, O_DIRECT, network filesystems under terabyte-scale reads) and how fast they move.
- You instrument before you optimize, and you can tell a real win from a benchmark artifact.
- 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

- Push throughput. Continuous batching, chunked prefill, speculative decoding with draft models, CUDA-graph capture, and scheduler tuning across the engine fleet. Find the ceiling on each GPU SKU, then raise it.
- Cut latency. Attack TTFT and inter-token latency separately; profile to find the actual bottleneck (compute, memory bandwidth, scheduling, storage, or network) and fix it, not the one you assumed.
- Get more out of the KV cache. Hierarchical and paged attention, prefix caching on heavy-reuse coding traffic, eviction policies, quantized KV. A lot of the unrealized throughput lives here, and you'll own it.
- Kill cold starts. Weight loaders (O_DIRECT, fastsafetensors-class), JIT and compile caches, shared-filesystem read paths. A 1.5 TB checkpoint has no business taking an hour to reach serving.
- Quantize without regressing quality. INT8/INT4, FP8/FP4 across weights, activations, and KV, measured on real workloads, not just perplexity benchmarks.
- Scale across nodes. Distributed inference topologies, network-aware placement, prefill/decode disaggregation, checkpointing strategies that don't bottleneck on storage or interconnect.
- Set the technical direction. Decide what we benchmark, what we adopt, and what we build ourselves. Bring the team along with strong writeups and reproducible experiments.

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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