In this role, you will collaborate with a diverse, cross-functional team to design and develop large-scale HD mapping algorithms, workflows, and data pipelines. Your work will directly impact our ability to efficiently map new cities and continuously update existing maps at scale, playing a critical role in accelerating our autonomous vehicle deployment.
In this role, you will:
- Design and build a high-quality, large-scale mapping dataset and data pipelines for training and evaluating machine learning models
- Design and build tools for model performance benchmarking and introspection
- Collaborate closely with machine learning engineers to define and refine data curation and model training strategies that drive measurable improvements in model accuracy and performance.
- Collaborate cross-functionally with a variety of teams working on things such as perception, planning, prediction, simulation, etc
Qualifications:
- BS or MS in Computer Science or related field and 3+ years of experience
- Proficient in Python, TypeScript, React
- Experience with large-scale, multi-modal datasets, and web tooling for model benchmarking and introspection
- Ability to identify, clean, and process datasets containing high levels of noise or ambiguous classifications
Bonus Qualifications:
- Proficient in C++
- Experience in computer vision, machine learning algorithms
- Knowledge of geospatial data and coordinate systems
What We Do
Zoox is an autonomous mobility company that was founded to provide a safer, cleaner, and more enjoyable future on the road. To achieve that goal, the company has spent the past 10 years creating a purpose-built robotaxi that gives the world a better way to ride.
Why Work With Us
At Zoox, we are working to solve one of the greatest technological challenges of our generation. From the beginning, we have been focused on our goal of reimagining transportation from the ground up. We are a mission-driven community of innovators working together to create a safer, cleaner, and more enjoyable future on the road.
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