Research Engineer, Code Agents Infra

Mistral
Palo Alto
Workplace: HybridFull timeFunction: Research & Scientific (R&D)Experience: 4+ yearsSkills: ["Profiling","Optimization","Working alongside researchers"]

Build and operate the end-to-end execution, training, and data infrastructure behind agentic models and coding assistants. You’ll design scalable synthetic data generation and ultra-fast training/RL execution environments, ranging from orchestrating 1M+ concurrent untrusted-code sandboxes to optimizing distributed trajectory collection and dataset workflows across hybrid and multi-cloud clusters. Work with Kubernetes-native controllers, container and isolation tech, and high-throughput execution engines, with on-call rotations for critical pipelines.

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FursaFursa
Mistral
Mistral
2 days ago

Research Engineer, Code Agents Infra

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Last checked: 3 hours agoStatus: Live

Job Summary

Build and operate the end-to-end execution, training, and data infrastructure behind agentic models and coding assistants. You’ll design scalable synthetic data generation and ultra-fast training/RL execution environments, ranging from orchestrating 1M+ concurrent untrusted-code sandboxes to optimizing distributed trajectory collection and dataset workflows across hybrid and multi-cloud clusters. Work with Kubernetes-native controllers, container and isolation tech, and high-throughput execution engines, with on-call rotations for critical pipelines.
Location: Palo Alto
Workplace: Hybrid
Employment Type: Full time
Job Function: Research & Scientific (R&D)
Seniority: Mid level

Key Responsibilities

  • •Design, deploy, and operate a high-throughput sandboxing platform executing LLM-generated untrusted code across 1M+ isolated environments for evaluation and interactive RL.
  • •Architect and scale synthetic code generation and agent data pipelines for trajectories, rollouts, and self-play data collection that power post-training and RL loops.
  • •Optimize agent training codebases and distributed execution runtimes to reduce rollout overhead, improve GPU utilization, and remove scaling bottlenecks.
  • •Reduce sandbox cold-start times using container warm pools plus snapshot/restore (e.g., CRIU, microVMs) and optimized image delivery layers across hybrid clusters.
  • •Implement Kubernetes-native custom controllers (CRDs) and queuing/resource allocation to route short-lived evaluation and execution tasks across diverse hardware fleets, while ensuring strong isolation and operational reliability with telemetry and self-healing.

Pay and Benefits

Perks:Health InsuranceParental LeaveRetirementRelocationWellness StipendMeal AllowanceCommuter Benefits

Key Requirements

  • •4+ years of experience in systems engineering, distributed systems, cloud infrastructure, or MLOps supporting LLM/RL workloads.
  • •Proven experience building high-throughput data processing and generation pipelines for large-scale datasets (e.g., Ray, Spark, distributed queues).
  • •Deep experience with Kubernetes and container technology, including writing custom K8s operators/controllers and working with Linux cgroups/namespaces.
  • •Advanced proficiency in Python, Go, C++, or Rust, with experience profiling and optimizing high-performance ML or backend systems.
  • •Hands-on experience with lightweight virtualization/container runtimes or WASM for sandboxing and isolation (e.g., Docker, gVisor, Firecracker).
Experience:4+ years
Skills:ProfilingOptimizationWorking alongside researchers
Tech Stack:PythonGoC++RustPyTorchRaySLURMKubernetesDockerCRIUMicroVMsGVisorFirecrackerWASMLinux cgroupsLinux namespacesSpark

Company Brief

Mistral
Develops state-of-the-art large language models and AI systems, offering models and developer tools for natural language understanding, generation, and enterprise AI integrations. Focuses on open research and production-ready model deployments.
Industry: AI & Machine Learning
Company Size: Medium (51 to 250 employees)
Growth: Early Stage Startup
Funding: Seed
Headquarters: Paris, France
Founded: 2023
WebsiteLinkedIn