Build and deploy the ML-based perception system (3D detection, BEV, tracking, sensor fusion) for L4 autonomous trucks, owning models from architecture through onboard deployment and safety validation. Requires 5+ years in ML perception/robotics, strong Python/C++/PyTorch, and classical perception fundamentals.
151k – 255k/yr
On-site5+ YOEML Engineering
About the role
Responsibilities
Design, train, and deploy deep learning models for 3D object detection across LiDAR, camera, and radar sensors.
Build multi-sensor fusion architectures that combine independent detection and classification streams.
Build and operate the data engine: shadow-mode comparison against production, automated data mining, and retraining loops.
Contribute to camera- and LiDAR-based localization perception under degraded conditions.
Take your work through the full safety validation pipeline before it operates on public roads.
Collaborate daily with behavior/planning engineers, systems and safety engineers, and validation teams.
Requirements
Bachelor's or Master's degree in Computer Science, Robotics, Electrical Engineering, or a related field.
5+ years of professional experience in ML-based perception, computer vision, or robotics.
Strong proficiency in Python and C++.
Hands-on experience training and deploying deep learning models in production (e.g., PyTorch).
Solid grounding in classical perception: multi-object tracking, state estimation, and 3D geometry.
Nice-to-Haves
Experience working in modern ML-based perception for autonomous systems.
Experience with multi-sensor calibration and fusion.
Experience optimizing models for onboard/embedded GPU inference.
Background in safety-critical or automotive software development.
Experience in a startup or fast-paced environment.
Compensation and Benefits
Base salary range: $151,000 - $255,000 USD annually.
Total compensation package may also include equity (options and/or restricted stock units), comprehensive health, dental, vision, life and disability insurance coverage, 401k retirement benefits with employer match, learning and wellness stipends, and paid time off.
Actual base salary influenced by experience, credentials, education, skills, interview performance, and level/scope of position.
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