Senior/Staff Machine Learning Engineer - Offline Driving Intelligence

Zoox is on an ambitious journey to develop a full-stack autonomous mobility solution for cities and safely deploy a robotaxi service. We are looking for a Senior Machine Learning Engineer to join our team to help find rare events and their likelihood.

This role is centered on applying cutting-edge machine learning to develop and enhance our validation processes. You will be instrumental in improving the efficiency and scalability of our testing by sampling across massive datasets where traditional methods no longer suffice.

By working with both real-world fleet logs and synthetic data, your work will directly impact how we validate software changes and ensure our robotaxi service is both safe and reliable. Your work will also significantly improve the speed and efficiency of our validation process, enabling Zoox to move faster and achieve more.

You will be part of an organization with strong leadership and a transparent, respectful culture that enables you to reach your full potential. This high-impact position offers opportunities for career growth through demonstrated achievement.

In this role, you will

Lead Technical Initiatives: You will apply modern machine learning, leveraging large-scale data, to critical validation problems at the intersection of ML and data science. You will serve as a key contributor and tech lead on a small, focused team.

Improve Feature Representation: You'll extend and refine the features and embedding space used by our models to better identify and cluster interesting driving scenarios. This involves applying first-principles thinking to derive new, impactful features.

Integrate AV Performance Data: You'll incorporate metrics and information on autonomous vehicle (AV) performance into the model to make its risk predictions more accurate and relevant.

Collaborate Cross-Functionally: You'll work closely with system safety, data science, software, and fleet operations teams to understand their needs and integrate improvements that directly support our validation efforts.

Qualifications

Experience: A PhD in a relevant field and/or 5+ years of experience working with machine learning models and data science methodologies in an industry setting.

Technical Skills: Expertise in machine learning concepts, including training deep learning models, evaluation, and optimization. Strong programming skills in Python and experience with relevant machine learning libraries (e.g., PyTorch, TensorFlow, Jax). Experience with large-scale data processing and distributed computing.

Domain Knowledge: Experience in robotics, autonomous vehicles, or a related field, with an understanding of challenges in perception, prediction, and planning.

Mindset : Proven ability to drive progress independently, lead technical projects, and apply critical thinking to solve practical problems.

Communication: Excellent communication skills and the ability to work effectively with cross-functional teams.

Bonus Qualifications

Real-world impact as demonstrated in patents, presentations, blog posts, and publications at at top ML conferences such as Neurips, ICML, CORL, and ICLR.

Familiarity with encoder-decoder or foundation models for prediction and planning.

Experience with test scripting and data analysis languages like SQL.

Familiarity with the challenges of fleet data collection and validation in the autonomous vehicle space.

Background in Bayesian optimization, online learning, and adaptive search.