About the work
You’ll solve autonomy’s hardest data challenges through applied ML and systems rigor:
- Architect hybrid systems combining deep learning and classical algorithms for scalable data curation and annotation.
- Design frameworks to quantify synthetic data’s real-world fidelity and improve synthetic data rendering quality.
- Build tools that automatically surface data gaps impacting perception model performance.
- Collaborate with autonomy engineers to turn raw sensor streams into targeted training priorities – addressing critical gaps that limit perception and autonomy performance
About You
- BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area.
- Experience:
- 4+ years of industry software engineering experience with Python fluency and C/C++ familiarity. Proven ability to lead cross-functional technical projects from design to completion.
- You possess practical experience in implementing ML solutions and enjoy integrating them into real-world systems. Your focus is on deploying impactful, integrated solutions rather than purely theoretical ML experimentation.
Bonus Points
- Familiarity working with synthetic or autonomous driving data.
- Experience building ML systems for robotic applications
Compensation: Base pay range $193,930 - $291,150/year, plus annual performance bonus, equity, and competitive benefits.