Senior Software Engineer, Perception ML Data

Nuro•Mountain View, CA

About The Position

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. Design and advance systems that: 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 Tailor Your Impact: High-Output Generalist: Work across autonomy, infrastructure, databases, simulation, and ML development, gaining domain knowledge in Robotics and ML. Robotics Expert: Build state of the art solutions for data discovery, auto-labeling, and synthetic generation/reconstruction in close collaboration with Infrastructure and Autonomy. 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

Requirements

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.

Nice To Haves

Familiarity working with synthetic or autonomous driving data.
Experience building ML systems for robotic applications

Responsibilities

Design and advance systems that leverage VLMs to curate geographically diverse datasets matching real-world driving distributions
Design and advance systems that develop high fidelity synthetic data frameworks across sensor modalities
Design and advance systems that 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