This Research Engineer, ML Platform job is with Mistral AI in California. Expect a hybrid setup: this is full-time work with regular office days. The employer does not list pay for this job, so raise it with the recruiter at the first call.
Here is the role as Mistral AI describes it.
Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.
We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.
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 regions.
You will work across the full ML lifecycle, from workload scheduling and capacity management to platform APIs, observability, and production operations. You will take ownership of critical systems and help turn complex infrastructure into reliable, self-service capabilities.
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.
Optimize Performance: Improve GPU utilization, scheduling latency, workload startup time, throughput, and infrastructure efficiency.
Build for Reliability: Develop observability, failure recovery, capacity planning, and operational tooling for critical ML workloads.
Operate What You Build: Participate in on-call rotations and troubleshoot issues across applications, schedulers, networking, storage, and GPU infrastructure.
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.
Are familiar with GPU infrastructure and technologies such as PyTorch, CUDA, NCCL, and high-performance networking.
Understand concepts such as quotas, priorities, preemption, gang scheduling, topology awareness, and workload admission.
Can 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.
We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.
For the most up-to-date details on benefits available in your location, please refer to our Benefits page.
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Mistral AI has not included pay in the posting.
It is hybrid. You would work partly from home and partly from the office in California.
The posting mentions at least 4 years of relevant experience and skills such as Python, Go, Kubernetes and PyTorch. Read the full description above for the complete list.
Use the apply button on this page. It takes you to Mistral AI's own application form, so your CV goes directly to their hiring team.
Mistral AI published the role on September 3, 2026. We check the employer's job board every day and take this page down when the role closes, so if you can read it, the job was still listed at our last check.
Source: Mistral AI careers page (jobs.ashbyhq.com). Applications go straight to the employer. We check the listing daily and remove it once it closes.
Mistral AI publishes its open roles on its own careers board. This page collects the Mistral AI jobs that are live right now. Each listing links to Mistral AI's application form, and we remove a listing once the employer closes it.
Industry: Technology
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