Machine Learning Staff Engineer – ADAS Online

We are building a high-performance ADAS Online team focused on advancing state-of-the-art machine learning and AI algorithms for scene understanding and environmental awareness. As ML Staff Engineer, you will drive the algorithmic direction of our present and future in-vehicle spatial awareness stack while remaining deeply hands-on in model design, experimentation, and performance improvement. You will contribute as a senior technical authority and mentor within a small, high-velocity team.

This spatial awareness stack will leverage modern transformer and end-to-end architectures to transform vehicle sensor data along with real-time 3D map data from the cloud into a 3D, semantically defined environment identifying all static and dynamic objects. This is a high-ownership technical role in a fast-moving, data-driven AI environment.

• Define and drive the technical direction for physical AI algorithms
• Define and execute on a technical roadmap towards state-of-the-art reinforcement learning using physical AI world models.
• Design, implement, and improve ML / vision transformer models for 3D awareness and planning. These include Gaussian Splatting (3DGS), Diffusion, object detection, multi-object tracking, semantic segmentation, and occupancy modeling
• Architect multi-modal fusion approaches (camera, LiDAR, RADAR) to build 3D environments
• Per the roadmap, identify where larger end-to-end models should replace more traditional approaches
• Apply advanced ML techniques (Transformers, representation learning, large-scale models) to improve perception performance
• Lead structured experimentation and benchmarking to deliver measurable gains in accuracy and robustness
• Translate research ideas into reliable, scalable ML solutions
• Provide technical guidance and mentorship to perception engineers

• 7+ years of experience in machine learning, vision transformers, diffusion, or computer vision
• Deep expertise in modern deep learning architectures
• Strong hands-on experience with PyTorch (or equivalent frameworks)
• Proven experience building and iterating on large-scale ML models
• Strong mathematical foundations in optimization and probabilistic modeling
• Track record of delivering measurable improvements in ML system performance
• Experience guiding technical decisions within a small engineering team

• Experience in autonomous systems or robotics perception
• Publications or patents in machine learning or perception
• Experience with 3D data representations (gaussian spatting, point clouds, BEV, voxel grids) and 3D engines like Unity
• Familiarity with large-scale training or foundation models
• Experience mentoring engineers in advanced ML topics