What You'll Do
- Own the strategy and execution of the Autonomy Data Flywheel, a core system enabling continuous AI improvement through real-world robotic delivery data.
- Define and prioritize data requirements for teams including AI, Perception, and Safety, ensuring alignment with operational goals.
- Collaborate with engineering to establish and meet performance benchmarks for data volume, quality, and system reliability.
- Develop and track data diversity metrics to ensure comprehensive coverage of urban sidewalk environments and edge cases.
- Partner with Operations and IT to enhance infrastructure at depots and in the cloud, supporting seamless data capture and processing.
- Design and maintain data retention practices that balance operational efficiency, safety, and model training needs.
Requirements
- Proven background in product or engineering roles focused on data infrastructure within robotics, autonomous vehicles, or Edge AI domains.
- Hands-on experience building and managing production-grade machine learning operations pipelines.
- Strong understanding of training data workflows, evaluation frameworks, and their impact on AI model performance.
Preferred Qualifications
- Direct experience with edge and depot data systems, including NAS infrastructure.
- Familiarity with ML Ops practices, active learning, and targeted data collection strategies.
Technical Environment
- Machine Learning Operations (ML Ops)
- AI and computer vision models
- Data pipeline architecture
- Edge computing systems
- Cloud infrastructure platforms
- Network-attached storage (NAS) setups
- Applied machine learning workflows
Work Environment
This role operates in a hybrid model, supporting remote work across the United States and Canada. While base locations are in the San Francisco Bay Area, flexibility is available within North American time zones. Compensation may vary based on location, experience, and role scope.
Our Culture
We value agility, collaboration, and technical rigor. Our team is diverse and mission-driven, focused on solving tangible urban logistics challenges through responsible innovation. We prioritize end-to-end user experience and maintain a respectful, inclusive environment where mindful problem-solving leads to real-world impact.
