NS
Niantic Spatial
AI Engineer

Technical Lead, Computer Vision

On-siteSeniorAI Engineerposted 6mo ago
Role summaryAI-generated

This role leads the development of advanced computer vision algorithms for high-fidelity 3D geospatial mapping, focusing on textured meshes, Gaussian Splats, and semantic understanding to power Niantic Spatial’s Large Geospatial Model. You’ll architect scalable solutions for real-world perception, bridging research insights with production-grade localization and reconstruction systems.

Skills required

About this role

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.

Job 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.

Job 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.

  • Work Location: This position requires 3 days per week in our San Francisco OR Sunnyvale office.

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