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Senior/Staff Software Engineer, ML Performance Optimization

Washington, United States (On-site)

Senior/Staff Software Engineer, ML Performance Optimization

1 Month ago • 4-8 Years • Artificial Intelligence

About the job

5 skills required for this role

Test your skills to join the top 1% applicants for this job

cpp

deep-learning

python

pytorch

innovation

Job Description

Zoox is seeking a Senior/Staff Software Engineer, ML Performance Optimization to lead our initiatives and make our Training and Inference platform that enables autonomous driving as fast and efficient as possible. You will work across all ML teams within Zoox and have the opportunity to significantly push the boundaries of how ML is practiced within Zoox. You will develop and execute a strategic vision for the ML Performance Optimization team, lead the design, implementation, and operation of cutting-edge ML Training and inference performance optimization techniques, collaborate with cross-functional teams to define requirements and align on architectural decisions, and enable engineers to grow their careers by providing technical guidance and mentorship.

Must have:

Strong experience with training frameworks like PyTorch
Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks
Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler
Proficient in Python and C++
Experience with model compression techniques to reduce model size and improve performance

Perks:

paid time off
unpaid time off
Zoox Stock Appreciation Rights
Amazon RSUs
health insurance
long-term care insurance
long-term and short-term disability insurance
life insurance

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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 ML and CV to make autonomous driving as seamless as possible.

The Opportunity

Are you excited to lead our ML Performance Optimization initiatives and make our Training and Inference platform that enables autonomous driving as fast and efficient as possible? You will get to work across all ML teams within Zoox - Perception, Prediction, Planner, Simulation, Collision Avoidance, and Advanced Hardware Engineering group 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, deep learning frameworks, and inference systems used by our applied research teams for in- and off-vehicle ML use cases. You will lead a team of strong software engineers and act as a force multiplier for our internal customers. This team has a lot of 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.

In this role, you will:

Develop and execute a strategic vision for the ML Performance Optimization team to unlock ML innovation in autonomous driving and rider experience.
Lead the design, implementation, and operation of cutting-edge ML Training and inference performance optimization techniques.
Collaborate closely with x-functional teams, including ML researchers, software engineers, data engineers, and hardware engineers, to define requirements and align on architectural decisions.
Enable the engineers in the team to grow their careers by providing technical guidance and mentorship.

Qualifications

Strong experience with training frameworks like PyTorch, leveraging GPUs efficiently for distributed model training.
Experience with GPU-accelerated inference using TensorRT, Ray Serve, or similar frameworks.
Experience using profiling tools like NVIDIA's Nsight or PyTorch's Profiler for identifying model training and serving bottlenecks.
Proficient in Python and C++
Experience with model compression techniques to reduce model size and improve performance.

Compensation

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. The salary range for this position is $200,000 to $332,000. A sign-on bonus may be offered as part of the compensation package. 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.

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.

View Full Job Description

$200.0K - $332.0K/yr (Outscal est.)

$266.0K/yr avg.

Seattle, Washington, United States

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