About the job
We’re a team of high-output generalists where ML and systems engineering converge to push autonomy performance forward. As a Perception ML Data Engineer, you’ll bridge machine learning innovation and autonomy infrastructure to ensure our models learn from the most relevant, diverse, and high-quality data. Your work will directly impact how autonomous systems understand rare scenarios, adapt to global geographies, and scale safely.
Responsibilities
Leverage VLMs to curate geographically diverse datasets matching real-world driving distributions
Develop high fidelity synthetic data frameworks across sensor modalities
Optimize ML-powered validation of data quality and model readiness
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
Qualifications
Minimum
BS in Computer Science, Robotics, Statistics, Physics, Math or another quantitative area.
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.
Preferred
Familiarity working with synthetic or autonomous driving data.
Experience building ML systems for robotic applications