Zoox is looking to improve its automated testing and validation processes for its full-stack autonomous mobility solution and robotaxi service by applying cutting-edge machine learning.
Requirements
- 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.
- 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.
Responsibilities
- 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 pioneer methods to understand the internal workings of our machine learning models, bridging the gap between "black box" models and systems engineering.
- You'll extend and refine the features and embedding space used by our models to better identify and cluster interesting driving scenarios.
- You'll incorporate metrics and information on autonomous vehicle (AV) performance into the model to make its risk predictions more accurate and relevant.
- You'll apply your data science expertise to optimize models and sampling methodologies.
- You will 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.
Other
- 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.
- Experience in robotics, autonomous vehicles, or a related field, with an understanding of challenges in perception, prediction, and planning.
- Proven ability to drive progress independently, lead technical projects, and apply critical thinking to solve practical problems.
- Excellent communication skills and the ability to work effectively with cross-functional teams.
- Familiarity with the challenges of fleet data collection and validation in the autonomous vehicle space.
