Software Engineer - Perception (Fallback Stack)
Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co.
We are an in-office company, and our expectation is that full-time employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. This in-office expectation does not apply to contractor positions
About the role
As a Perception Software Engineer on the Autonomy team, you will build the perception system for L4 autonomous trucks operating at highway speeds. The stack combines state-of-the-art machine learning - multi-modal 3D detection, BEV representations, learned tracking - with rigorous classical perception: state estimation, multi-object tracking, and sensor fusion. You will own problems end-to-end, from model architecture and training through onboard deployment and on-road validation. This is safety-critical software: every capability passes formal simulation, closed-course, and safety sign-off gates before it reaches public roads.
You will collaborate daily with behavior/planning engineers, systems and safety engineers, and validation teams.
At Applied Intuition, you will
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
Characterize perception failure modes, build targeted evaluation sets, and hill-climb system-level metrics
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
We're looking for someone who has
Bachelor's degree in Computer Science, Robotics, Electrical Engineering, or a related field
4+ 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 have
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 at Applied Intuition for eligible roles includes base salary, equity, and benefits. Base salary is a single component of the total compensation package, which may also include equity in the form of 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. Note that benefits are subject to change and may vary based on jurisdiction of employment.