Engineering Manager, Onboard Execution Performance
Company websitezoox.com (Zoox, opens in new tab)
Base salary $237K to $322K
Employer-published annual base salary, USD. Advertised base range in Foster City, CA +1 more
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationFoster City, CA +1 more
- WorkplaceHybrid
- Status at last checkSource accepting applications when checked
- Last checked (12 min ago) · observed since
All places named
- Foster City, CA
- Seattle, WA
About this job
Zoox is building the world's most advanced self-driving hardware and software solution. The efficiency demands of such a system require an expert fine tuning of both the compute hardware architecture as well as the algorithms and middleware that runs on it to achieve maximum throughput at the most optimal power levels.
The Software Performance team keeps the main onboard computer healthy and performant to ensure that our on-bot autonomy software meets its latency targets while also accommodating its safety goals. We are responsible for the efficient allocation of onboard compute resources (CPU/GPU/Memory/accelerators) ensuring contention is minimized and the autonomy stack executes harmoniously.
As the Onboard Execution team leader within the Performance team, you will lead a highly specialized team of software performance engineers in aggressively optimizing on-bot software runtime. This is a high impact role that sits at the intersection of ML Platform, Core, and Autonomy Software - ensuring that efficient compute utilization is a central tenet emphasized across the organization.
In this role, you will:
- Manage a team of 8-to-10 engineers focused on optimizing the software performance of the on-bot autonomy software stack.
- Partner with Autonomy, ML Platform, Middleware, Sensors, and Hardware Architecture teams to meet strict system response (latency) and compute utilization targets.
- Achieve alignment between cross-functional leaders across the Software organization on compute resource allocations and budgets.
- Hire, grow, and develop talent in the realm of performance optimization.
- Build self-service force-multiplier frameworks & workflows that empower developers to deliver efficient & performant software.
Qualifications
- BS in computer science, computer engineering, or related field
- Proficiency with C++ & concurrent (parallel) execution systems
- Deep knowledge and understanding of the Linux operating system and GPU accelerated execution
- Proven track record of cross-functional collaboration and leadership alignment
- Prior experience managing teams of five or more full-time software engineers
Bonus Qualifications
- Hands-on experience with GPU runtime frameworks (CUDA, TensorRT, or XLA)
- Experience in robotics, aviation, automotive, or high-performance computing space
- Have shipped real-time or near-real-time software autonomy or robotics software in production
- Experiencing debugging and optimizing CPU, GPU kernels, and ML models using tools like NVIDIA Nsight Systems and
- Compute, Perfetto or similar compute observability tools
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.
Source: Zoox on Lever · View original posting (opens in new tab)
This listingEngineering Manager, Onboard Execution Performance$237K to $322K
Senior/Staff Software Engineer - Embedded Linux Operating Systems$237K to $322K
Senior Software Engineer - Planner GPU Compute$237K to $298K
Engineering Manager, Simulation Scenario Generation$237K to $322K
Manager - Model Evaluation$237K to $338K
Senior / Staff Software Engineer - Learned Trajectory Machine Learning Engineer$242K to $349K
Senior / Staff Machine Learning Engineer - 3D Sensor Simulation$242K to $348K
Chart as a table
| Role | Published base |
|---|---|
| Engineering Manager, Onboard Execution Performance (this listing) | $237K - $322K |
| Senior/Staff Software Engineer - Embedded Linux Operating Systems | $237K - $322K |
| Senior Software Engineer - Planner GPU Compute | $237K - $298K |
| Engineering Manager, Simulation Scenario Generation | $237K - $322K |
| Manager - Model Evaluation | $237K - $338K |
| Senior / Staff Software Engineer - Learned Trajectory Machine Learning Engineer | $242K - $349K |
| Senior / Staff Machine Learning Engineer - 3D Sensor Simulation | $242K - $348K |
Observed changes
- First observed on the employer's board
- Began meeting the published-salary criteria
- Observed since
- Employer-stated posting date
- 2026-04-17
Where this pay comes from
Location as the employer wrote it: Foster City, CA • Seattle, WA
USD 237,000 – 322,000 · per-year-salary · salaryRange
- Method
- Structured pay field published by the employer
- Checked
- Rule set
- us-base-v2
The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
