Senior Machine Learning Engineer - Autonomy Validation

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.

• 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 Model Interpretability
  :
  You'll pioneer methods to understand the internal workings of our machine learning models. 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.
• 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.

• 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, Tensor Flow, 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.

• Real-world impact as demonstrated in publications, patents, presentations, blog posts, etc.
• Familiarity with encoder-decoder or foundation models for prediction and planning.
• Experience with test scripting and data analysis languages like SQL.
• Experience with techniques for machine learning model interpretability and explainability.
• Familiarity with the challenges of fleet data collection and validation in the autonomous vehicle space.

$229,000 - $276,000 a year

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,…