Zoox is transforming mobility-as-a-service by developing a fully autonomous, purpose-built fleet designed for AI to drive and humans to enjoy.
Zoox is transforming mobility with fully autonomous, electric vehicles designed from the ground up for a driverless future. Our mission is to make transportation safer, more sustainable, and accessible to everyone. At Zoox, innovation, collaboration, and a bold vision for the future drive everything we do.
Zoox’s internship program offers hands-on experience with cutting-edge technology, mentorship from some of the industry’s brightest minds, and the opportunity to make meaningful contributions to real projects. We seek interns who demonstrate strong academic performance, engagement beyond the classroom, intellectual curiosity, and a genuine interest in Zoox’s mission.
As an intern at Zoox, you’ll be working alongside those that architect, design and develop our state of art hardware and signal processing systems for enabling autonomous mobility. At Zoox, you’ll collaborate with a team of world class engineers with diverse backgrounds in areas such as AI and ML, robotics, and simulation. You’ll master new technologies while working on code, algorithm and research in your area so expertise to create and refine the key systems that move Zoox forward.
As an intern in the DSP team you will be working on design and implementation of signal processing and machine learning algorithms related to radars, depth cameras, lidars and audio subsystems. We are looking for curious and highly mathematical minds to work with us.
Compensation:
The monthly salary range for this position is $6,500 to $9,500. Compensation will vary based on geographic location and level of education. Additional benefits may include medical insurance, and a housing stipend (relocation assistance will be offered based on eligibility).
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
[email protected] 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.
Program Requirements
Currently pursuing a B.S., M.S., or other advanced degree in a relevant engineering field.Plan to return to school after completing the internship.Maintain good academic standing.Available to commit to a 12-week internship beginning in May or June 2026.At least one prior industry internship, co-op, or relevant project experience.Willing and able to relocate to the Bay Area, California, for the duration of the internship.Interns may not use proprietary Zoox information in university theses, publications, or share it outside of Zoox.Qualifications
Proficiency in C, C++, Python and/or MatlabCourse work in Digital Signal Processing, Circuits and systems, Stochastic filtering, Probability and Statistics, Random Signals and NoiseHands-on experience related to algorithm design in one or more areas related to radar and lidar processing. Ability to bring your best can-do attitude daily while solving hard problems with constraints Bonus Qualifications
Experience with Radar Signal Processing and understanding of the radar pipeline (ADC data, FFT, Range-Doppler maps, CFAR, angle-of-arrival estimation).Experience with processing 4D radar data or raw sensor data.HW and SW experience or research related to radar processing and/or radar perceptionExperience with machine learning techniques such as deep neural nets (DNN, CNN, LSTM-RNN, LLMs, VLMs, Transformers) and traditional statistical modeling/feature extraction techniques (GMM, HMM, NMF / spectrograms etc.)