Principal Machine Learning Engineer, 3D Geometric Vision

Overview

As a Principal Engineer on the Model Foundations team, you will lead the design and deployment of 3D foundation models for autonomous driving. You will prototype architectures, build scalable data and supervision pipelines, and bring successful ideas to real-vehicle deployment. You will shape geometric vision and world modeling while collaborating with researchers and engineers across perception, data, and deployment. This role offers ownership of high-impact problems at scale and a clear path from research to production.

Pay / Benefits

• hybrid working model
• equity
• relocation support and visa sponsorship
• learning and development budgets
• comprehensive benefits including health insurance
• team socials and wellbeing partnerships

Responsibilities

• Design and train 3D foundation models and world models using large-scale driving data
• Develop architectures for 3D perception, geometry, reconstruction, and world modeling across space and time
• Build scalable data generation and auto-labeling pipelines for geometric supervision
• Develop offline SLAM and 3D reconstruction systems and pipelines with large-scale sensor data
• Explore geometry-aware tokenization and representation learning across cameras, viewpoints, time, and modalities
• Develop foundation vision models using camera, radar, LiDAR, and other sensors
• Explore video and generative modeling for scene dynamics from driving data
• Train and evaluate models at scale on distributed compute and iterate rapidly
• Create automated evaluation and ground-truth systems for geometric consistency and driving performance
• Optimize and deploy models into production autonomous-driving systems and set technical direction

Key requirements

• Deep expertise in 3D computer vision or geometric vision with experience in multi-view geometry, neural rendering, reconstruction, implicit representations, or world modeling
• Proven ability to design, train, and evaluate modern deep-learning models at scale (PyTorch or equivalent)
• Strong foundations in geometry, linear algebra, probability, optimization, and 3D transformations, plus software engineering in Python and C++
• Track record of taking difficult research problems to working systems with data, training, evaluation, or deployment pipelines
• Principal-level technical leadership: identifying high-leverage problems, setting direction, architectural decisions, and elevating teams

• technical leadership
• collaboration across cross-functional teams
• strategic problem solving
• 3D computer vision
• multi-view geometry
• NeRFs and neural rendering

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