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Salary: USD 151,000 - 255,000 / annual
Applied Intuition is a $15B-valued AI infrastructure company powering autonomous systems across automotive, defense, trucking, construction, mining, and agriculture. As a Perception Software Engineer on the Autonomy team, you will design and deploy the perception system for L4 autonomous trucks operating at highway speeds.
You will own end-to-end perception problems: designing and training deep learning models for 3D object detection across LiDAR, camera, and radar sensors; building multi-sensor fusion architectures; characterizing failure modes and building evaluation sets; and operating the data engine with shadow-mode comparison, automated data mining, and retraining loops. You will also contribute to camera- and LiDAR-based localization under degraded conditions and take your work through the full safety validation pipeline before deployment on public roads.
The role combines state-of-the-art machine learning—multi-modal 3D detection, BEV representations, learned tracking—with rigorous classical perception including state estimation, multi-object tracking, and sensor fusion. This is safety-critical software: every capability passes formal simulation, closed-course, and safety sign-off gates.
You will collaborate daily with behavior/planning engineers, systems and safety engineers, and validation teams. The company operates primarily in-office (5 days/week from Sunnyvale) with flexibility for occasional remote work.
Required: Bachelor's or Master's in Computer Science, Robotics, Electrical Engineering, or related field; 5+ years 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, 3D geometry).
Nice to have: experience in modern ML-based perception for autonomous systems, multi-sensor calibration and fusion, model optimization for embedded GPU inference, safety-critical or automotive software development, or startup experience.
