# Research Engineer, Code Agents Infra at Mistral AI

- Company: Mistral AI
- What the company does: The most powerful AI platform for enterprises. Customize, fine-tune, and deploy AI assistants, autonomous agents, and multimodal AI with open models. Backed by General Catalyst, Index and Lightspeed.
- Company website: https://mistral.ai/
- Type: Startups (AI role)
- Level: Mid level
- Location: Palo Alto
- Work setup: Remote
- Posted: 2026-08-04
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/mistral.ai/af7c3cf8-f4f4-4a16-80e9-fdfb0c14104d
- Page: https://www.1752.vc/careers/jobs/mistral-ai-research-engineer-code-agents-infra/

## About the role

This role focuses on building and operating the end-to-end execution, training, and data infrastructure that powers Mistral’s agentic models and coding assistants. You will be a core contributor to our agent research stack: designing scalable systems for synthetic data generation, building ultra-fast training and RL execution environments, and maintaining high-throughput execution engines.

## What they're looking for

- 4+ years of experience in Systems Engineering, Distributed Systems, Cloud Infrastructure, or MLOps supporting LLM/RL workloads
- Data & Pipeline Engineering: Proven experience building high-throughput data processing and generation pipelines for large-scale datasets (e.g., Ray, Spark, custom distributed queues)
- Deep experience with Kubernetes & Container Tech: Strong expertise writing custom K8s operators/controllers, managing Linux cgroups/namespaces, and optimizing Docker image layers and distribution systems
- High-Performance Software Engineering: Advanced proficiency in Python, Go, C++ or Rust, with a track record of profiling and optimizing high-performance ML or backend systems codebases
- Sandboxing & Isolation Technologies: Hands-on experience with lightweight virtualization, container runtimes, or WASM (e.g., Docker, gVisor, Firecracker)
- Queueing & Scheduling: Deep familiarity with task queue systems, resource schedulers, and low-latency queuing architectures for high-volume, short-lived workloads

Tags: Science