Mistral AIResearch Engineer, ML Platform
Mistral AI is hiring a Research Engineer, ML Platform for a remote role open to applicants in United States. Level rates it ; you can apply on Mistral AI.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include PyTorch, Kubernetes and GPUs.
- Level classifies it as a senior role.
- Level lists 234 other open roles at Mistral AI.
AI in this role
Build and operate large-scale ML infrastructure, training platforms, and distributed GPU compute systems.
About Mistral
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.
The Role
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.
What You Will Do
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.
What We'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.
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.
What We Offer
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.
Privacy Policy
Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.
How we rate this
Research Engineer, ML Platform at Mistral AI rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Tell me about a research question you investigated. What did you find?
- Tell me about a project where ml infrastructure was part of your work. What did you do?
- Tell me about a project where distributed systems was part of your work. What did you do?
- Tell me about a project where platform engineering was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Fine-tuning, AI Research, ML Infrastructure, Distributed Systems, and Platform Engineering. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
Get your CV reviewed before you apply
Send your CV and the role you want next. Your review arrives by email within 3 days.
Get new remote research jobs (Builds AI ●●●●) by email
One email a week, each job rated for how much AI is in the work. No recruiter spam, unsubscribe in one click.
Free.
Similar roles
Research roles that build AI, at other companies.
What kind of AI work fits you?
Answer 12 practical questions in about three minutes. Get a simple profile, the work it points to, and live roles to explore next.
Find my next step