Research Engineer, Inference Foundation

Lindus Health Limited; Warszawa; Hybrid; PLN 180,000 - 300,000; Full time

Job summary

Mistral is seeking a senior engineer to own the core of its inference stack, including the engine and orchestration, with a focus on performance and scale. You will help power frontier model training and ensure elastic capacity across production workloads.

The role spans production LLM serving, engine development, and capacity engineering in a hybrid setup. Candidates should have deep ML/LLM infra experience and strong systems expertise.

Qualifications

• Experience building and running ML/LLM services atScale with latency targets.
• Hands-on with inference engines such as vLLM, sgLang, TensorRT-LLM, or similar.
• Strong grasp of inference internals and distributed serving architectures.

Responsibilities

• Implement and optimize the core inference stack and orchestration.
• Own release processes for serving stack with automated gates.
• Drive improvements upstream when appropriate.

Skills

Python

PyTorch

Kubernetes

CUDA/NCCL

InfiniBand/RDMA

vLLM/sglang

Go/TensorRT-LLM

Tools

NCCL

Containers

Networking

Job description

About Mistral

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.

We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

The Role

The Inference Foundation team owns the core of Mistral's inference stack: the inference engine and its orchestration, from the feature set and configuration that serve our models in production to the release machinery that keeps the stack current and production-grade.

This is a hybrid position spanning production LLM serving, engine and platform development, and capacity engineering. You will work on three intertwined problems:

1. Optimize the inference stack at scale — feature development and fixes in the engine and orchestrator, squeezing more throughput and lower latency out of every GPU under strict quality-of-service targets.

2. Make capacity elastic — scaling up and down should be cheap and fast, not a performance cliff.

3. Power the training of our frontier models — high-performance serving that keeps RL and post-training loops running at full speed.

What You Will Do

Inference engine & orchestration

• Develop and fix the core of the inference stack — engine and orchestrator — including feature selection, configuration, and tuning for maximum performance at scale

• Own the release process for the serving stack: validated, regression-free releases through automated performance gates and progressive rollout

• Drive improvements and fixes upstream when the open-source engine is the right place for them

Performance & capacity at scale

• Optimize serving efficiency across the fleet — driving down pod startup time, tackling cold-cache regressions on scale-up, smarter caching and offloading

• Optimize and maintain the optimal serving topology — overlap communication and transfers with computation, ensure optimal placement, connectivity, and routing

Serving for frontier training

• Build the serving infrastructure that powers RL and post-training for our frontier models

• Optimize inference performance across the full spectrum of our workloads

What We're Looking For

• Experience building and running ML/LLM services at scale, with clear latency and availability targets

• Hands-on experience with inference engines such as vLLM, SGLang, TensorRT-LLM, or others

• A solid grasp of inference internals: prefill vs. decode, KV-cache behavior, batching, scheduling, speculative decoding, parallelism strategies

• Familiarity with distributed and disaggregated serving architectures

• Comfortable debugging across the full stack — CUDA/NCCL, kernels, containers, networking, storage

• Python for systems tooling and backend services; PyTorch

• Kubernetes for running infrastructure at scale

• GPU and networking fundamentals: CUDA runtime, NCCL, InfiniBand/RDMA

It Would Be Great If You Have

• Demonstrated vLLM/sglang know-how — upstream contributions, or a track record of running in demanding production environments

• Hardware-aware optimization for various model architectures

• Experience serving MoE models at scale (expert parallelism, expert placement/load balancing)

• CUDA/Triton kernel development; Nsight Systems/Compute profiling

• Rust and/or C++ in production systems

What We Offer

We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.

For the most up-to-date details on benefits available in your location, please refer to our Benefits page.

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