ML Platform Engineer: GPU Infra & Orchestration

Precision Labs; Palo Alto (CA); Hybrid; USD 170,000 - 230,000; Full time

Benefits offered by this job

Healthcare coverage

Retirement plans

Relocation support

Wellness programs

Meal and transportation allowances

Job summary

Mistral AI is seeking an experienced engineer to build and operate the ML platform powering large-scale training, evaluation, and batch inference. You will develop infrastructure that enables researchers and engineers to run distributed GPU workloads across clusters, hardware types, and regions.

You will work across the full ML lifecycle, from workload scheduling and capacity management to platform APIs, observability, and production operations.

Qualifications

• 4+ years of experience in ML infrastructure, distributed systems, or related field.
• Proficiency in Python or Go for production-grade distributed systems.
• Strong Kubernetes knowledge including controllers, operators, CRDs, scheduling.
• Familiar with GPU/MLOps tooling and high-performance networking.

Responsibilities

• Build services, APIs, and tooling for large-scale ML workloads.
• Orchestrate GPU workloads across clusters with topology-aware placement.
• Manage compute capacity across heterogeneous GPUs and regions.
• Enable multi-cluster execution with data locality considerations.
• Improve researcher experience with self-service workflows.
• Optimize GPU utilization and scheduling latency.
• Develop observability and reliability tooling for ML workloads.
• Participate in on-call rotations and troubleshoot across infra.

Skills

ML infrastructure

Distributed systems

Kubernetes platform engineering

Python

Go

GPU workloads

Performance optimization

Tools

Kueue

Karpenter

Volcano

Kyverno

Job description

Mistral AI is seeking an experienced engineer to build and operate the ML platform powering large-scale training, evaluation, and batch inference. You will develop infrastructure that enables researchers and engineers to run distributed GPU workloads across clusters, hardware types, and regions.

You will work across the full ML lifecycle, from workload scheduling and capacity management to platform APIs, observability, and production operations.