Technical Lead, Computer Vision at Niantic Spatial in San Francisco, CA

Job description

• Application
• At Niantic Spatial, we’re building the future of geospatial AI. Powered by a proprietary database of over 30 billion posed images and a groundbreaking third-generation digital map, our mission is to develop spatial intelligence that helps both humans and machines better understand, navigate, and engage with the physical world. Our high-fidelity mapping technology unlocks a new dimension of interaction—laying the foundation for AI to truly comprehend and operate within real-world environments. Join us as we build a living model of the world that people and machines can talk to.
• As a Tech Lead for the Applied Computer Vision Algorithms Team, you’ll help drive our “Reconstruct”, “Understand” and “Localization” capabilities. This team is responsible for creating the high-fidelity visual and semantic maps—specifically textured, semantic meshes, and Gaussian Splats— as well as localization maps that allow our Large Geospatial Model (LGM) to perceive the world with human-like precision. Closely working with the R&D and product teams your work will bridge the gap between cutting-edge theory and real-world utility, turning complex geospatial data into a persistent sense of space for the next generation of AI and robotics.

Responsibilities

• Applied Research & Implementation: Actively translate top-tier research papers (e.g., from CVPR, ECCV, NeurIPS) into production-grade features within our tech stack.
• Technical Leadership: Lead the design and implementation of 3D reconstruction pipelines, focusing on Structure from Motion (SfM) and high-fidelity mesh generation as well as 3D gaussian splats.
• Algorithm Optimization: Develop and optimize Gaussian Splatting quality algorithms and general ML code for high-performance execution on CPU and GPU.
• Production Implementation: Write and maintain high-performance, shader-based production code in C++ for Android and Linux environments.
• Technical Strategy & Mentorship: Work with engineering leadership to define the technical roadmap and quarterly objectives for the Applied CV Team; provide high-level mentorship and code governance to elevate the team’s technical bar.
• Cross-Functional Collaboration: Partner with the Research and Spatial Solutions teams to turn strategic goals into actionable plans.
• Quality & Benchmarking: Drive decision-making creating high quality data that allows the accurate spatial grounding of AI queries with structural, semantic and location specific knowledge.

Requirements

• Years of Experience: 8+ years of professional experience in Computer Vision, Machine Learning, or a related field (or 6+ years with a PhD in a relevant domain).
• Education: Bachelor’s degree in Computer Science, Engineering, or a related technical field; Master's or PhD preferred.
• Core Technical Skills: Strong proficiency in C/C++ and Python for production-level software development.
• Specialized Expertise: Proven experience in 3D Computer Vision/ML, specifically with Structure from Motion (SfM), 3D reconstruction, and Gaussian Splatting rendering techniques.
• Hardware Optimization: Demonstrated ability to optimize algorithms for GPUs in Android or Linux environments.
• Graphics Knowledge: Experience with computer graphics and C++ shader-based implementations.
• Technical Leadership Experience: Previous experience tech leading a team of computer vision engineers in a high-growth environment.

Skills

Required

• 3D Computer Vision/ML
• Structure from Motion (SfM)
• 3D reconstruction
• Gaussian Splatting rendering techniques

Benefits

• The total compensation package for this position includes a new hire offer base salary range of $256.5K – $315K • Offers Equity • Offers Bonus • bonus + equity + benefits.
• Individual pay within this salary range is determined by work location and additional factors, including assessed job-related skills, experience, and relevant education or training. Your recruiter can answer any questions about new hire total compensation during the hiring process. An overview of benefit offerings for your location can be found on the careers page.