Research Engineer, ML Platform

Job summary

Responsibilities

This role focuses on building and operating the ML platform that powers large-scale training, evaluation, and batch inference at Mistral AI. You will develop the infrastructure that enables researchers and engineers to run distributed GPU workloads reliably across clusters, hardware types, and…

• Build the ML Platform: Develop services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference.
• Orchestrate GPU Workloads: Build systems for queueing, admission control, quotas, priorities, preemption, and topology-aware placement.
• Manage Compute Capacity: Improve how heterogeneous GPU resources are provisioned, allocated, and utilized across clusters.
• Enable Multi-Cluster Execution: Place workloads based on capacity, data locality, hardware requirements, and organizational priorities.
• Improve Researcher Experience: Create self-service workflows that make distributed workloads easy to launch, observe, debug, and reproduce.

What they're looking for

• Have 4+ years of experience in ML infrastructure, distributed systems, Kubernetes platform engineering, or a related field.
• Are proficient in Python or Go and comfortable working with production-grade distributed systems.
• Have strong Kubernetes knowledge, including controllers, operators, CRDs, scheduling, networking, storage, and resource management.
• Understand technologies such as Kueue, Karpenter, Volcano, and Kyverno, and the problems they address in workload scheduling, provisioning, and policy enforcement.
• Understand distributed ML workloads, including training, fine-tuning, evaluation, checkpointing, and batch inference.

Tech stack

• Kubernetes
• Python
• Go
• PyTorch
• CUDA