Machine Learning Platform Internship/Co-op
Foster City, CA
Software – Motion Planning & Control /
Internship/Co-op /
On-site
This position involves the development of an automated machine learning (AutoML) system. Our system uses large amounts of real-world driving data combined with high-fidelity simulation to improve driving quality and development efficiency. The candidate will work cross-functionally with engineers in driving AI/autonomy, simulation, and data science, and bring state-of-the-art machine learning and optimization applications to production.
Technical Qualifications & Language Requirements
- Currently enrolled MS/PhD in Machine Learning or Computer Science
- Strong programming skills in Python
- Knowledge of containerization and orchestration tools (e.g., Docker)
- Familiarity with cloud platforms such as AWS
- Basic understanding of machine learning concepts and frameworks (e.g., Pytorch)
- Experience with system design and understanding of complex systems
Compensation
The monthly salary range for this position is $6,500 to $9,500. Compensation will vary based on role, degree level and type, and benefits will be offered based on eligibility. Additional benefits may include medical insurance, 401k, and a housing stipend.
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.
