Pay
In line with similar jobs
$177K–$270K against $166K–$240K, the middle half of what similar US postings state.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Software Engineer, ML Training Optimization
Foster City, CA
Live, first seen by us 19 days ago- Pay
USD 177,000–270,000/year — pay source
Salary 177,000 – 270,000 USD per year About Zoox
Read the full posting- Work setting
On-site — work setting source
Work setup in the employer’s posting: onsite
From the employer’s posting- Employment
Full-time — employment source
Employment type Full-time
Employment type in the employer’s posting: Full-time
From the employer’s posting
What you’ll work on
Full postingOptimize distributed training efficiency using parallelism, mixed precision, and gradient checkpointing
Improve RL framework performance for driving foundation models
Profile and debug training throughput bottlenecks across the stack
Implement custom kernels and build benchmarks to validate performance gains
Define requirements and architecture for large-scale training infrastructure with researchers and engineers
From the employer’s posting
Design, implement, and operate cutting-edge ML training performance optimization techniques
Work on RL framework and RL performance optimization for driving foundation model
Profile and debug training throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.
Implement custom kernels and build benchmarks to track and validate training-performance optimizations across the stack.
Collaborate closely with ML researchers, data engineers, and hardware engineers to define requirements and align on architectural decisions for large-scale training infrastructure.
What you’ll bring
All qualificationsCore experience
- 4+ years of total experience, including 2+ years working on large-scale model training platforms.
- Experience with distributed training frameworks (Megatron-LM, TorchTitan), efficiently utilizing GPU/accelerator clusters at scale.
- Experience with RL framework such as Verl/MILES and RL algorithms(GRPO, PPO)
- Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.
- Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks.
- Proficient in Python or C++.
Qualification wording
4+ years of total experience, including 2+ years working on large-scale model training platforms.
Experience with distributed training frameworks (Megatron-LM, TorchTitan), efficiently utilizing GPU/accelerator clusters at scale.
Experience with RL framework such as Verl/MILES and RL algorithms(GRPO, PPO)
Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.
Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks.
Proficient in Python or C++.
About Amazon
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 16, 202619 days ago
- Recorded sightings
- 35
- Last seen by us
- Nov 5, 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 19
Similar jobs usually come down between day 6 and day 70.
At Amazon, jobs usually come down by about day 69.
From 4,442 similar postings that came down, and 20 of Amazon’s. Coming down is not always a hire.
Amazon is hiring
24 open jobs
Open jobs by the week we first saw them, last 12 weeks.
- ML Engineer hiring is picking up
- Takes jobs down at a usual pace
- 4% of open jobs were reposted
See 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.
- C++
- Python
- PyTorch
Where the posting names them
- Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks. - Proficient in Python or C++. - Experience with ML compilers, performance tooling, or hardware/software co-design (e.g., GPU/TPU/accelerator architectures).
- Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training. - Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks. - Proficient in Python or C++.
Your next step
Complete your application on jobs.lever.co. The employer’s form will show what is required.
Applying through Lever
- No account needed: one form, and Lever reads your resume into the profile recruiters search.
Already applied? Track this application
About Amazon
- People
- About 1,207,759 in 2024.Benefit plan filings (Form 5500)
- Company
- Listed as AMZN.
- H-1B visas
- 6,217 approved for new hires in fiscal 2025.US Citizenship and Immigration Services
- Layoff notices
- Notices this year and last cover 988 people. The latest was filed January 29, 2026.State WARN notices
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 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
Zoox is on a mission to reimagine transportation and ground-up build autonomous robotaxis that are safe, reliable, clean, and enjoyable for everyone. We are still in the early stages of deploying our robotaxis on public roads, and it is a great time to join Zoox and have a significant impact in executing this mission. The ML Platform team at Zoox plays a crucial role in enabling innovations in large-scale Foundation models, VLMs, and VLAs to make autonomous driving as seamless as possible.
The Opportunity
Are you excited to drive our ML Training Optimization initiatives and make the models that power autonomous driving trains faster, cheaper, and at greater scale? You will work with SOTA accelerators and cutting-edge techniques in distributed training, parallelism strategies, mixed-precision training and RL, especially for VLA and VLM models that are multi-modal — partnering closely with all the Autonomy teams within Zoox (Perception, Prediction, Planner, Simulation, Collision Avoidance) and pushing the boundaries of how ML training is practiced at Zoox.
We build and operate the base layer of ML tools, model development, and training systems that our applied research teams use for in- and off-vehicle ML use cases. You will work alongside a team of strong software engineers and act as a force multiplier for our internal customers. This team has many growth opportunities as we expand our robotaxi deployments and venture into new ML domains. If you want to learn more about our stack behind autonomous driving, please look here. If you want to learn more about our ML Infrastructure, here is one of our past talks at re:Invent.
In this role, you will:
-
Design, implement, and operate cutting-edge ML training performance optimization techniques — distributed training (data/tensor/pipeline parallelism), mixed-precision, gradient checkpointing, and GPU kernels — to scale our VLM, VLA, and Foundational models training efficiency.
-
Work on RL framework and RL performance optimization for driving foundation model
-
Profile and debug training throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.
-
Collaborate closely with ML researchers, data engineers, and hardware engineers to define requirements and align on architectural decisions for large-scale training infrastructure.
-
Implement custom kernels and build benchmarks to track and validate training-performance optimizations across the stack.
Qualifications
-
4+ years of total experience, including 2+ years working on large-scale model training platforms.
-
Experience with distributed training frameworks (Megatron-LM, TorchTitan), efficiently utilizing GPU/accelerator clusters at scale.
-
Experience with RL framework such as Verl/MILES and RL algorithms(GRPO, PPO)
-
Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.
-
Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks.
-
Proficient in Python or C++.
-
Experience with ML compilers, performance tooling, or hardware/software co-design (e.g., GPU/TPU/accelerator architectures).
Note: You do not have to meet all the requirements below to be considered for this position:
Base Salary Range
There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.
Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.
Salary
177,000 – 270,000 USD per year
About Zoox
Zoox is developing the first ground-up, fully autonomous vehicle fleet and the supporting ecosystem required to bring this technology to market. Sitting at the intersection of robotics, machine learning, and design, Zoox aims to provide the next generation of mobility-as-a-service in urban environments. We’re looking for top talent that shares our passion and wants to be part of a fast-moving and highly execution-oriented team.
Accommodations
If you need an accommodation to participate in the application or interview process please reach out to accommodations@zoox.com or your assigned recruiter.
A Final Note:
You do not need to match every listed expectation to apply for this position. Here at Zoox, we know that diverse perspectives foster the innovation we need to be successful, and we are committed to building a team that encompasses a variety of backgrounds, experiences, and skills.
Employment type
Full-time
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Salary 177,000 – 270,000 USD per year About Zoox
- Location & working pattern
Foster City, CA
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.