Research Engineer, ML Platform

Mistral AI

Research Engineer building Mistral AI’s distributed GPU platform for large-scale training, evaluation, and inference. Improving Kubernetes orchestration, reliability, and researcher self-service workflows.

Posted 9/3/2026full-timePalo Alto • California • 🇺🇸 United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing ML infrastructure and distributed systems, with a strong focus on Kubernetes, GPU resource management, and performance diagnostics. Proficient in creating self-service workflows and operational tooling to enhance the efficiency of critical ML workloads.

Highest-signal resume keywords

Kubernetes Platform EngineeringPython or Go ProficiencyDistributed Systems ExperienceGPU Infrastructure FamiliarityML Workload Understanding

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonGoKubernetesDistributed SystemsGPU Resource ManagementPyTorchCUDANCCLObservabilityCapacity Planning

Soft Skills

Problem DiagnosisDeveloper Experience FocusAdaptability

Tools & Technologies

KueueKarpenterVolcanoKyverno

Industry Keywords

ML InfrastructureBatch InferenceWorkload AdmissionTopology AwarenessScheduling Latency

Tech Stack

Tools & technologies

Distributed SystemsGoKubernetesPythonPyTorch

About the role

Key responsibilities & impact

Develop services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference
Build systems for queueing, admission control, quotas, priorities, preemption, and topology-aware placement of GPU workloads
Improve provisioning, allocation, and utilization of heterogeneous GPU resources across clusters
Place workloads across multiple clusters based on capacity, data locality, hardware requirements, and organizational priorities
Create self-service workflows for launching, observing, debugging, and reproducing distributed workloads
Improve GPU utilization, scheduling latency, workload startup time, throughput, and infrastructure efficiency
Develop observability, failure recovery, capacity planning, and operational tooling for critical ML workloads
Participate in on-call rotations and troubleshoot applications, schedulers, networking, storage, and GPU infrastructure

Requirements

What you’ll need

4+ years of experience in ML infrastructure, distributed systems, Kubernetes platform engineering, or a related field
Proficiency in Python or Go
Experience working with production-grade distributed systems
Strong Kubernetes knowledge, including controllers, operators, CRDs, scheduling, networking, storage, and resource management
Understanding of Kueue, Karpenter, Volcano, and Kyverno
Understanding of distributed ML workloads, including training, fine-tuning, evaluation, checkpointing, and batch inference
Familiarity with GPU infrastructure, 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
Interest in developer experience and turning complex infrastructure into simple, reliable interfaces
Ability to thrive in an ambiguous, fast-moving environment shaped by frontier AI research
Willingness to commute to Palo Alto or San Francisco offices 4 days a week

Benefits

Comp & perks

Healthcare coverage
Parental leave
Retirement plans
Relocation support
Wellness programs
Meal allowances
Transportation allowances
Other location-specific perks