Senior Machine Learning Engineer, Autonomy Validation

Job Description

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 improve our automated testing and validation processes.

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 contribute to the speed and efficiency of our validation process, allowing Zoox to go fast 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.

Base Salary Range

There are three major components to compensation for this position: salary, Amazon Restricted Stock Units (RSUs), and Zoox Stock Appreciation Rights. A sign-on bonus may be offered as part of the compensation package. The listed range applies only to the base salary. Compensation will vary based on geographic location and level. Leveling, as well as positioning within a level, is determined by a range of factors, including, but not limited to, a candidate's relevant years of experience, domain knowledge, and interview performance. The salary range listed in this posting is representative of the range of levels Zoox is considering for this position.

Zoox also offers a comprehensive package of benefits, including paid time off (e.g. sick leave, vacation, bereavement), unpaid time off, Zoox Stock Appreciation Rights, Amazon RSUs, health insurance, long-term care insurance, long-term and short-term disability insurance, and life insurance.

In this role, you will:

Lead Technical Initiatives: You will apply modern machine learning, including advanced techniques like encoder-decoder models, 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 Model Interpretability: You'll pioneer methods to understand the internal workings of our machine learning models, bridging the gap between "black box" models and systems engineering. This work will be crucial for building trust and ensuring the safety of our autonomous systems.

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 leveraging a deep understanding of the autonomous driving stack and 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.

Optimize with Data Science: You'll apply your data science expertise to optimize models and sampling methodologies. This includes analyzing large datasets to identify patterns and critical insights that improve the model's ability to find risky or valuable scenarios for simulation.

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 model training, 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.