Senior/Staff Software Engineer, ML Training Performance

Zoox

Foster City, CA

On-site

Who this role is best for

Aimed at senior software engineers with ML infrastructure expertise who can lead end-to-end projects and collaborate across multiple teams.

Best fit for

Candidates with end-to-end project leadership in large-scale ML training platforms
— “7+ years of total experience, including 4+ years on large-scale model training platforms”
Individuals experienced in optimizing RL frameworks for autonomous systems
— “Own the technical strategy and multi-quarter roadmap for RL framework and RL performance optimization”
Engineers skilled in GPU kernel development and performance profiling
— “Build custom kernels and benchmarks that track and validate performance gains across the stack”
Professionals with a background in distributed training and communication libraries
— “Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap”

Things to consider

The role requires leading technical strategy over multiple quarters, not just short-term tasks
— “Own the technical strategy and multi-quarter roadmap for RL framework”
Collaboration with researchers and hardware engineers is a core part of the role
— “Partner with ML researchers, data engineers, and hardware engineers”

How to stand out

Demonstrate experience in optimizing training performance for large-scale models like VLMs and VLAs
— “Lead the design, implementation, and operation of training performance optimizations”
Showcase leadership in defining requirements for ML infrastructure systems
— “Partner with ML researchers, data engineers, and hardware engineers to define requirements”
Highlight expertise in profiling tools and resolving training bottlenecks
— “Strong command of profiling tools to find and resolve training bottlenecks”
Emphasize your ability to mentor and raise team technical standards
— “Mentor engineers and raise the team’s technical bar through design reviews”

Pace · Fast PacedCollaboration · HighAutonomy · HighDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

lead the design, implementation, and operation of training performance optimizations
own the technical strategy and multi-quarter roadmap for RL framework and RL performance optimization
drive profiling and root-cause analysis of throughput bottlenecks
mentor engineers and raise the team’s technical bar

Typical background

7+ years of total experience4+ years on large-scale model training platformsdeep experience with distributed training frameworks

Skills & requirements

Required

Distributed Training FrameworksGpu/accelerator ClustersRL FrameworksProfiling ToolsPython Or C++ML CompilersHardware/software Co-design

Preferred

Verl/milesGrpo, PPO

Stack & domain

PythonC++Distributed Training FrameworksRL FrameworksCollective Communication LibrariesProfiling ToolsML CompilersHardware/software Co-designLeadershipProblem-solvingCommunicationInfluenceDesign ReviewsBest PracticesAutonomous DrivingMLRobotaxis

About the role

Original posting from Zoox via Lever

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 Performance Optimization initiatives and make our ML models that enable autonomous driving as fast and efficient as possible? You will get to work with SOTA accelerators, cutting-edge techniques in distributed training, quantization, distillation, and pruning, among other things, working closely with all the Autonomy teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox.
We build and operate the base layer of ML tools, model development, and serving 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::
Lead the design, implementation, and operation of training performance optimizations (data/tensor/pipeline parallelism, mixed precision, gradient checkpointing, GPU kernels) to scale VLM, VLA, and Foundation model training.
Own the technical strategy and multi-quarter roadmap for RL framework and RL performance optimization for driving foundation models.
Drive profiling and root-cause analysis of throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.
Partner with ML researchers, data engineers, and hardware engineers to define requirements and make architectural decisions for large-scale training infrastructure.
Build custom kernels and benchmarks that track and validate performance gains across the stack.
Mentor engineers and raise the team’s technical bar through design reviews and best practices.
Qualifications:
Note: You do not have to meet all the requirements below to be considered for this position:
7+ years of total experience, including 4+ years on large-scale model training platforms, with a record of leading projects end to end.
Deep experience with distributed training frameworks (Megatron-LM, TorchTitan) and efficiently utilizing GPU/accelerator clusters at scale.
Experience with RL frameworks (Verl/MILES) and algorithms (GRPO, PPO).
Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.
Strong command of profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to find and resolve training bottlenecks.
Proficiency in Python or C++.
Experience with ML compilers, performance tooling, or hardware/software co-design (GPU/TPU/accelerator architectures).
Ability to influence across teams and communicate technical tradeoffs to both researchers and engineers.
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.
Follow us on LinkedIn
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.

Source: Zoox careers (Lever)