Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,383 postings in the last year. Stated ranges, not salaries paid.
Research Engineer, ML Platform
Palo Alto, California, United States
Actively hiring, first seen by us today- Pay
- Pay not listedSimilar roles state $166K–$240K
- Work setting
Hybrid — work setting source
Work setup in the employer’s posting: Hybrid
From the employer’s posting- Employment
Full-time — employment source
Employment type in the employer’s posting: FullTime
From the employer’s posting
What you’ll work on
Full postingYou will develop the infrastructure that enables researchers and engineers to run distributed GPU workloads reliably across clusters, hardware types, and regions.
Build the ML Platform: Develop services, APIs, controllers, and tooling for training, evaluation, fine-tuning, and batch inference.
You will work across the full ML lifecycle, from workload scheduling and capacity management to platform APIs, observability, and production operations.
From the employer’s posting
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.
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
Benefits in the posting
Full benefits wording- 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.
- Privacy Policy
From the employer’s posting.
About mistral.ai
Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute.
In the employer’s words · Read in context
Posting record
Report an errorActively hiringFirst seen by us today, still listed, and not a repost of an earlier posting.
- Status in our records
- Live
- First seen by us
- Nov 9, 2026today
- Recorded sightings
- 1
- Employer says posted
- Sep 3, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 0
Early. Similar jobs usually come down between day 7 and day 72.
From 4,777 similar postings that came down. Coming down is not always a hire.
mistral.ai is hiring
18 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- Kubernetes
- PyTorch
- Go
Where the posting names them
- 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.
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.
- 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.
Your next step
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Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.
Each bar is one week of new postings. The darkest bars are the last 6 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
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.
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
No pay amount identified in the saved description.
- Location & working pattern
Palo Alto, California, United States
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.