via Workable
$120K - 160K a year
Develop and optimize perception ML modules for autonomous vehicles, including data collection, model training, and validation.
Requires 4+ years in computer vision and ML, experience with 3D reconstruction, detection, classification, semantic segmentation, and deployment in latency-sensitive systems, with proficiency in C++ and Python.
Onsite in Foster City, CA | at least 3 days onsite The Perception team is looking for a machine learning engineer to develop cutting-edge Computer Vision modules to enhance on-board perception of robots fleet, directly impacting safety and fleet efficiency. In this role, the ideal candidate will work on the full design and development cycle, including data collection, data set creation, machine learning models design and implementation. Responsibilities: Design and develop in-cabin Computer Vision ML modules for safety critical perception and monitoring applications Implement Perception model architectures and sophisticated training techniques Build high quality datasets leveraging all the inputs from our sensor stack and the overall large scale data. Validate and optimize your solutions using real-world driving scenarios, directly contributing to the safety and reliability of our autonomous system Qualifications: Bachelor's Degree in Computer Science and/or Computer Engineering with 4+ years of experience Modern Computer Vision and Machine Learning background, specifically on 3D reconstruction, detection, classification, semantic segmentation Experience with training and deploying Deep Learning models Background in C++ and/or python Excellent communication skills Bonus Qualifications: Experience with any of the following: Self-driving industry experience Experience delivering ML model integration in latency-sensitive systems Experience with CUDA code Health Care Plan (Medical, Dental & Vision) Life Insurance (Basic, Voluntary & AD&D) Paid Time Off (Vacation, Sick & Public Holidays) Training & Development Retirement Plan (401k, IRA)
This job posting was last updated on 2/16/2026