Job Description Research Engineer - Computer Vision & Machine LearningWant to build vision systems that let machines understand the physical world as naturally as we do?
This role sits within a highly technical team developing a new class of computing devices where perception, language, and interaction are tightly integrated. Vision is a core capability. Your work will directly influence how machines see, reason about space, and collaborate with humans in real-world environments.
You'll join a specialist vision group working across 3D computer vision and machine learning. The problems sit at the boundary between learned models and physical reality, including gaze tracking, SLAM, multi-camera geometry, and systems that explicitly model optics, refraction, and light transport. The focus is on geometry-aware, physically grounded approaches rather than purely pixel-driven modelling.
This is a hands-on research engineering role. You'll move between reading papers, building and training models, designing datasets, running controlled experiments, and deploying onto real hardware. You'll work closely with firmware and hardware teams to ensure models operate reliably on-device.
Your work will include:
- Developing ML models across 3D perception, tracking, and spatial understanding
- Designing model architectures, training pipelines, evaluation frameworks, and inference systems
- Working with large-scale, multi-camera and sensor-rich datasets
- Translating state-of-the-art research into robust, production-ready systems
- Creating new approaches when existing methods do not meet performance or physical constraints
You'll have genuine technical ownership. The team values clear thinking, strong experimental discipline, and the ability to make informed bets on promising ideas.
You'll likely bring end-to-end experience building computer vision and ML models, alongside strong familiarity with modern research in 3D or geometry-aware vision. Hands-on experience with PyTorch or JAX is expected, as is comfort working with complex datasets. The ability to operate independently in ambiguous environments is important, as is clear communication across research, hardware, and product teams.
A Bachelor's degree or higher in computer science, machine learning, computer vision, applied mathematics, or a related field is required. A Master's or PhD is a plus, particularly if you've worked on geometry-aware or physically informed modelling approaches. Experience deploying ML systems into real products or working in high-ownership startup environments would be valuable.
Compensation: $190,000 - $320,000 base (depending on experience) + equity
Benefits: 401(k) matching, 100% employer-paid health, vision, and dental insurance, unlimited PTO and sick time, medical FSA matching
Location: San Francisco, on-site collaboration required
If you're motivated by building geometry-aware vision systems that connect AI to the physical world in meaningful ways, we'd like to hear from you!
All applicants will receive a response.
