Research Engineer, ML Platform

Sourced from feeny.ai

Mistral is hiring a Research Engineer for its ML Platform team to build and operate the infrastructure powering large-scale training, evaluation, and batch inference. You will develop services, APIs, and controllers for distributed GPU workloads across clusters, hardware types, and regions, and own critical systems end to end. The role spans the full ML lifecycle, from workload scheduling and capacity management to observability and production operations.

• Build services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference
• Orchestrate GPU workloads with queueing, admission control, quotas, priorities, preemption, and topology-aware placement
• Improve provisioning, allocation, and utilization of heterogeneous GPU resources across clusters
• Enable multi-cluster execution based on capacity, data locality, hardware requirements, and priorities
• Create self-service workflows so researchers can launch, observe, debug, and reproduce distributed workloads
• Optimize GPU utilization, scheduling latency, workload startup time, throughput, and infrastructure efficiency
• Build observability, failure recovery, capacity planning, and operational tooling for critical ML workloads
• Participate in on-call rotations and troubleshoot issues across applications, schedulers, networking, storage, and GPU infrastructure

• 4+ years of experience in ML infrastructure, distributed systems, Kubernetes platform engineering, or a related field
• Proficiency in Python or Go with production-grade distributed systems
• Strong Kubernetes knowledge including controllers, operators, CRDs, scheduling, networking, storage, and resource management
• Familiarity with Kueue, Karpenter, Volcano, and Kyverno and the problems they address
• Understanding of distributed ML workloads including training, fine-tuning, evaluation, checkpointing, and batch inference
• Familiarity with GPU infrastructure and technologies such as PyTorch, CUDA, NCCL, and high-performance networking
• Understanding of quotas, priorities, preemption, gang scheduling, topology awareness, and workload admission
• Ability to diagnose performance and reliability problems across software, orchestration, networking, storage, and hardware
• Care about developer experience and enjoy turning complex infrastructure into simple, reliable interfaces
• Thrive in an ambiguous, fast-moving environment shaped by frontier AI research