Salary
💰 $210,000 - $289,000 per year
Tech Stack
PythonPyTorchSQLTensorflow
About the role
- Lead Technical Initiatives: apply modern machine learning, including encoder-decoder models, to validation problems and serve as a tech lead on a small team.
- Improve Model Interpretability: pioneer methods to understand internal workings of ML models to build trust and ensure safety.
- Improve Feature Representation: extend and refine features and embedding space to better identify and cluster driving scenarios.
- Integrate AV Performance Data: incorporate AV performance metrics to make risk predictions more accurate and relevant.
- Optimize with Data Science: apply data science to optimize models and sampling methodologies across large datasets.
- Collaborate Cross-Functionally: work with system safety, data science, software, and fleet operations teams to support validation efforts.
Requirements
- 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.
- 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.
- 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.
- Bonus: Real-world impact as demonstrated in publications, patents, presentations, blog posts, etc.
- Bonus: Familiarity with encoder-decoder or foundation models for prediction and planning.
- Bonus: Experience with test scripting and data analysis languages like SQL.
- Bonus: Experience with techniques for machine learning model interpretability and explainability.
- Bonus: Familiarity with the challenges of fleet data collection and validation in the autonomous vehicle space.