Senior Software Engineer - Planner GPU Compute
At a glance
Senior Software Engineering role at Zoox. Boston · full-time · $237,000–$298,000 base.
techroles summary, based on the employer's posting.
What you'll do
- Profile GPU performance to find bottlenecks and decide where planner algorithms can be improved
- Help adapt the motion planner to GPU architectures with different capabilities and resource limits
- Reduce machine-learning inference latency and GPU memory use in planner models
- Advise planner engineers on CUDA and GPU performance as their technical specialist
What you bring
- A computer science degree or related qualification, plus at least eight years of experience
- Strong CUDA skills on recent GPU designs, including kernel debugging and optimization with tools such as Nsight
- Strong C++ skills, experience with large codebases, and comfort developing in Linux
- Background building, debugging, and profiling complex multi-process systems such as robotics or game engines
Who this fits
This senior role suits an engineer with deep CUDA performance expertise and experience working in complex software systems. The position is based in Boston and is hybrid.
From the employer
Zoox made the world’s first purpose-built commercial robotaxi, combining advanced self-driving hardware and software. In order to ensure safe operation, low latency and consistent resource utilization are critical. The Planner Compute team is responsible for the performance of the largest single software component within the autonomy stack, the motion planner.
The Planner Compute team is looking for an expert in GPU performance. You will instrument, monitor, analyze and optimize GPU-based algorithms that are performance-critical for our solution. The scope for GPU usage ranges from machine learning inference to custom CUDA kernels written specifically for the motion planner.
In this role, you will:
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Analyze performance metrics to identify GPU hotspots and optimizations
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Contribute to the adaptation of our current planner to multiple GPU architectures with different resources
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Optimize ML models for latency and GPU memory usage
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Support engineers within Planner as a subject matter expert on CUDA & GPU performance
Qualifications
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BS in computer science or related field and 8+ years of experience.
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Strong knowledge of CUDA as applied to recent GPU microarchitectures (e.g., Ampere, Blackwell) and experience debugging/optimizing GPU kernels using tools like Nsight.
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Strong knowledge of C++ and experience in large code bases, comfortable in Linux development environments.
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Experience in development, debugging, and profiling of complex multiprocess systems (e.g., robotic systems, game engines).
Bonus Qualifications
- GPU kernel development experience
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