Technical Lead, Computer Vision
Niantic SpatialBase Salary
$257k - $315k/yr
Responsibilities
- Translate research papers from venues such as CVPR, ECCV, and NeurIPS into production-grade features.
- Lead the design and implementation of 3D reconstruction pipelines using Structure from Motion, high-fidelity mesh generation, and 3D Gaussian Splats.
- Develop and optimize Gaussian Splatting quality algorithms and machine learning code for high-performance CPU and GPU execution.
- Write and maintain shader-based production code in C++ for Android and Linux.
- Define the Applied Computer Vision team’s technical roadmap and quarterly objectives with engineering leadership.
- Provide technical mentorship and code governance to raise the team’s engineering standards.
- Partner with Research and Spatial Solutions teams to turn strategic goals into actionable plans.
- Drive high-quality data creation and benchmarking for accurate spatial grounding of AI queries.
Requirements
- At least 8 years of professional experience in computer vision, machine learning, or a related field, or at least 6 years with a relevant PhD.
- Bachelor’s degree in computer science, engineering, or a related technical field; a master’s degree or PhD is preferred.
- Strong proficiency in C, C++, and Python for production software development.
- Proven experience with 3D computer vision and machine learning, including Structure from Motion, 3D reconstruction, and Gaussian Splatting rendering techniques.
- Experience optimizing algorithms for GPUs in Android or Linux environments.
- Experience with computer graphics and C++ shader-based implementations.
- Previous experience tech leading a team of computer vision engineers in a high-growth environment.
Benefits
- Requires working 3 days per week in the San Francisco or Sunnyvale office.
Categories
About Niantic Spatial
First, our founders brought digital mapping to the world. Next, we invented global-scale AR. Now we're building the real-world foundation model for people, AI and robots. We're building for the 80% of economic activity that takes place beyond our screens: robots that lose GPS and drift off course, defense teams with no shared 3D picture of where they operate, facilities where downtime costs millions. Unlike LLMs, which understand language, we build computer-vision models that understand physical space. And unlike generated environments, our models are geometrically accurate and have real-world coordinates - which is what makes physical AI precise and trustworthy. We work across three capabilities for our customers: reconstructing physical spaces as AI-native digital twins using cheap, off-the-shelf cameras; locating and orienting – “localizing” – machines and people within those spaces; and enabling AI to understand and answer questions about the real world.