Lead Zoox’s ML performance optimization team and roadmap for scalable training and inference. Oversee ML platform design, distributed training, GPU utilization, memory and communication optimization, model compression, profiling, and low-latency vehicle inference. Hire and mentor engineers while collaborating with ML research, software, data, product, and hardware teams to deliver measurable performance improvements across autonomous driving systems.
Zoox is on a mission to reimagine transportation and build autonomous robotaxis from the ground up that are safe, reliable, clean, and enjoyable for everyone. With bidirectional driving capabilities and four-wheel steering, our vehicle allows us to maneuver through compact spaces and change directions without needing to reverse. We are at a critical inflection point as we scale our robotaxi deployment, and it is a great time to join Zoox and have a significant impact on executing our mission.
Our growing ML performance engineering leadership team is looking for an Engineering Manager, ML Performance Optimization. The centralized ML performance team at Zoox plays a crucial role in enabling innovations across all our ML research teams to develop and deploy models across our robotaxi and cloud infrastructure and to advance cutting-edge training and inference optimization techniques.
The Opportunity
We are working on many interesting challenges to enable rapid experimentation and scale our multi-modal Foundation models and RL infrastructure, and ensure these models run efficiently on our vehicles, meeting our latency targets. You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation and our Advanced Hardware Engineering group, and have the opportunity to significantly push the boundaries of ML scaling and acceleration at Zoox.
You will lead a team of strong ML performance engineers and this team has many growth opportunities as we expand our geofences at U.S markets and venture into new ML domains. 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:
Vision: Develop and execute a strategic vision and roadmap for ML Training and Inference Performance Optimization, ensuring scalability, reliability, and performance to support autonomous driving.
Technical acumen: Lead the design, implementation, and operation of a robust and efficient ML platform to enable the training, validation, serving, optimization and monitoring of ML models.
ML Performance Optimization: Drive end-to-end performance optimization for large-scale model training and inference, including distributed training efficiency, GPU utilization, memory and communication optimization, model compression (quantization, pruning, distillation), and low-latency on-vehicle inference that meets strict real-time and compute budgets.
Hiring: Attract, hire, and inspire a diverse world-class engineering team, fostering a culture of innovation, collaboration, and excellence.
Partnership: Collaborate closely with cross-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
Mentorship: Enable engineers on the team to grow their careers by providing the right opportunities and clear, timely feedback.
Qualifications
- 8+ years of relevant experience, including 3+ years of management experience managing engineers.
- Strong technical background in ML performance optimization, such as distributed training strategies (data, tensor, pipeline parallelism, FSDP/ZeRO), mixed-precision training, kernel-level optimization (CUDA, Triton), compiler stacks (torch.compile, XLA, TVM), quantization, and profiling/benchmarking across GPU and embedded accelerators.
- Experience building user-friendly ML Infrastructure that enabled large-scale model training and high-throughput, low-latency serving use cases.
- Experience with training frameworks like PyTorch, JAX, etc., leveraging GPUs for distributed model training.
- Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
- Proven track record of extensive cross-functional collaboration, partnering with research, product, hardware, and platform teams to align priorities, influence technical direction, and deliver measurable performance improvements across organizational boundaries.
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 [email protected] 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.
Zoox Foster City, California, USA Office
4000 E 3rd Ave, Foster City, CA, United States, 94404
Zoox Foster City, California, USA Office
1149 Chess Drive, Foster City, CA, United States, 94404
Zoox Fremont, California, USA Office
47540 Kato Road, Fremont, CA, United States, 94538
Zoox San Francisco, California, USA Office
60 Broadway St, San Francisco, CA, United States, 94111
Similar Jobs
Artificial Intelligence • Big Data • Cloud • Security • Software • Cybersecurity • Infrastructure as a Service (IaaS)
Conduct application security assessments, threat modeling, code reviews, and manual penetration testing across web applications, APIs, cloud services, appliances, and AI-enabled features. Coordinate external penetration tests, validate vulnerabilities, track remediation, and verify fixes. Improve SAST, SCA, secrets detection, container scanning, SBOM, supply-chain, and CI/CD security workflows while advising Engineering on secure design and risk prioritization.
Top Skills:
Ci/CdCloud ServicesContainer ScanningCyclonedxKubernetesLinuxOwaspPenetration TestingSastSbomScaSecrets DetectionSpdxThreat Modeling
Financial Services
Coordinates group air travel programs from intake and setup through post-event follow-up. Serves as a liaison among clients, travel advisors, and internal teams; manages timelines, documentation, bookings, traveler activity, arrivals, departures, reports, quality control, and stakeholder updates. Requires three or more years of travel-industry experience, GDS or self-booking tool proficiency, Microsoft Office skills, strong communication, and multitasking abilities.
Top Skills:
Global Distribution Systems (Gds)ExcelMicrosoft OutlookMicrosoft WordSelf-Booking Tools
Financial Services
Manage branch-level business operations for J.P. Morgan Advisors, including project execution, Financial Advisor onboarding and offboarding, client and employee communications, process efficiency, and issue resolution. Collaborate with Risk, Controls, Legal, Compliance, Supervisory Management, and other functional teams to support growth, improve communication, and enhance advisor and client experiences.
Top Skills:
ExcelMicrosoft OutlookMicrosoft PowerpointMicrosoft Word
What you need to know about the San Francisco Tech Scene
San Francisco and the surrounding Bay Area attracts more startup funding than any other region in the world. Home to Stanford University and UC Berkeley, leading VC firms and several of the world’s most valuable companies, the Bay Area is the place to go for anyone looking to make it big in the tech industry. That said, San Francisco has a lot to offer beyond technology thanks to a thriving art and music scene, excellent food and a short drive to several of the country’s most beautiful recreational areas.
Key Facts About San Francisco Tech
- Number of Tech Workers: 365,500; 13.9% of overall workforce (2024 CompTIA survey)
- Major Tech Employers: Google, Apple, Salesforce, Meta
- Key Industries: Artificial intelligence, cloud computing, fintech, consumer technology, software
- Funding Landscape: $50.5 billion in venture capital funding in 2024 (Pitchbook)
- Notable Investors: Sequoia Capital, Andreessen Horowitz, Bessemer Venture Partners, Greylock Partners, Khosla Ventures, Kleiner Perkins
- Research Centers and Universities: Stanford University; University of California, Berkeley; University of San Francisco; Santa Clara University; Ames Research Center; Center for AI Safety; California Institute for Regenerative Medicine
