
About Zoox
Revolutionizing transportation with autonomous vehicle technology
Key Highlights
- Raised $1.2 billion in Series B funding
- Headquartered in Foster City, CA with 1001+ employees
- Innovative vehicle design with independent wheels and no front/back
- Part of a $2 trillion industry projected by 2030
Zoox, headquartered in Foster City, CA, is pioneering autonomous vehicle technology with a unique driverless ecosystem. The company has raised $1.2 billion in Series B funding and employs over 1,000 people. Its innovative vehicle design, revealed in December 2020, features independent wheels and a b...
🎁 Benefits
Zoox offers work-from-home opportunities, comprehensive health insurance, and generous paid parental leave. Employees enjoy unlimited flexible paid ti...
🌟 Culture
At Zoox, the culture is centered around innovation in autonomous transportation, with a focus on safety and enjoyment. The company promotes a collabor...

Machine Learning Engineer • Intern
Zoox • Foster City - On-Site
Skills & Technologies
Overview
Zoox is hiring a Machine Learning Engineer Intern for their Perception team to develop advanced multimodal large language models and enhance scenario understanding for autonomous vehicles. You'll work with technologies like Python and TensorFlow in Foster City.
Job Description
Who you are
You are a driven individual with a strong academic background in computer science, engineering, or a related field — your passion for machine learning and computer vision sets you apart. You have a solid understanding of machine learning principles and are eager to apply your knowledge in real-world scenarios, particularly in the context of autonomous vehicles. Your curiosity and aptitude for problem-solving enable you to tackle complex challenges effectively.
You possess experience with programming languages such as Python, and you are familiar with machine learning frameworks like TensorFlow and PyTorch — your technical skills allow you to contribute meaningfully to projects from day one. You thrive in collaborative environments and are excited about the opportunity to work alongside industry experts who can mentor you throughout your internship.
What you'll do
As a Machine Learning Engineer Intern at Zoox, you will be involved in developing and fine-tuning machine learning models that enhance the perception capabilities of autonomous vehicles. You will work on the Offline Driving Intelligence team, where you will leverage driving data to create models that can identify hazards and interpret driving restrictions. Your contributions will directly impact the safety and productivity of Zoox's autonomous systems.
You will also have the opportunity to collaborate with the Perception Attributes team, where you will collect and generate datasets for vehicle classification and semantic enrichment. In this role, you will design and frame machine learning problems for real-world scenarios, training and evaluating state-of-the-art models focused on computer vision. Your work will include experimenting with optimizing large ML models for real-time deployment, utilizing techniques such as quantization and token merging.
Throughout your internship, you will gain hands-on experience with cutting-edge technology and have the chance to validate your algorithms in real-world conditions. You will be part of a team that is dedicated to pushing the boundaries of what is possible in autonomous driving, and you will leave with valuable insights and skills that will benefit your future career.
What we offer
At Zoox, you will be part of an innovative and inclusive culture that values curiosity and collaboration. Our internship program is designed to provide you with hands-on experiences and mentorship from some of the brightest minds in the industry. You will have the opportunity to work on meaningful projects that contribute to the future of transportation, and you will be supported in your professional development throughout your time with us. We encourage you to apply even if your experience doesn't match every requirement, as we value diverse perspectives and backgrounds.
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