Machine Learning Staff Engineer
Role details
Job location
Tech stack
Job description
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.
What You'll Do
- 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
Requirements
- 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 tea, * 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
Benefits & conditions
A competitive compensation package, of course. Time and resources to grow and develop, including a personal development budget and paid leave for learning days, as well as paid access to e-learning resources such as O'Reilly and LinkedIn Learning. Time to support life outside of work, with enhanced parental leave plus paid leave to care for loved ones and volunteer in local communities.
