Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Leads the Device SDK and Mobile Engineering teams, owning technical strategy, architecture, native SDKs, spatial computing runtimes, and iOS and Android applications. This hands-on leader manages engineers, contributes production code, reviews architecture and code, guides AI-native development practices, and drives performance, portability, scalability, quality, and delivery. The role partners with research, platform, and product leadership across mobile, XR, robotics, and edge-computing initiatives while recruiting and developing engineering talent.

Build and maintain cloud infrastructure, developer platforms, CI/CD pipelines, edge services, and GPU compute capacity. Improve reliability through monitoring, alerting, SLOs, incident response, and automation. Manage containers, authentication systems, source control, and build infrastructure while participating in on-call operations and applying AI-assisted tools to infrastructure engineering.

The Technical Lead for Computer Vision will implement advanced algorithms, mentor engineers, and optimize code in C++ and Python while collaborating across teams.

Train and evaluate PyTorch computer vision models for visual localization, including feature matching, image retrieval, pose regression, and scene-coordinate regression. Build large-scale image and 3D data pipelines, run experiments on GPU infrastructure, maintain localization benchmarks, and support model export, inference optimization, deployment, and monitoring. Collaborate with research and testing teams to improve production model performance.

Lead engineering for a real-to-sim platform supporting embodied AI and robot learning. Own the technical roadmap and requirements, prototype and ship critical features, benchmark policy transfer, diagnose sim-to-real failures, and evaluate robotics systems in real-world conditions. Collaborate with researchers, product leaders, customers, and test labs while advancing simulation, reinforcement learning, visual perception, and 3D scene representation capabilities.

Build and operate developer-facing products for embodied AI, including APIs, UIs, packages, documentation, backend services, asset workflows, integrations, and asynchronous processing systems. Convert spatial reconstruction and mapping prototypes into production capabilities, support customer simulator and deployment integrations, diagnose reliability issues, and influence the product roadmap. The role requires full-stack product engineering, customer collaboration, and experience with 3D assets or comparable binary formats.

Build and maintain production-grade automation for real-world AI evaluations. The role owns end-to-end workflow orchestration, environment setup, artifact capture, reproducible datasets, scorecards, diagnostics, and release-triggered testing. It also develops agent- and LLM-driven workflows to evaluate customer use cases at scale, converting manual experiments into reliable standing protocols and actionable findings.

Own and characterize a fleet of robotic hardware and capture devices, engineer controlled physical test environments, build instrumentation and ground-truth references, and integrate physical runs into automated evaluation systems. Execute robot bring-up, teleoperation, device-in-the-loop testing, and sim-to-real experiments. Automate calibration, data offload, and condition logging while maintaining safety procedures, fleet operations, documentation, and reproducible test protocols.

Productionize computer vision and 3D reconstruction systems for embodied AI. Own spatial output quality, robustness, evaluation infrastructure, performance, throughput, cost, and reliability. Diagnose real-world failures involving calibration, scale, coordinate frames, and scene complexity. Collaborate with research, product, engineering, and customers to deliver production-ready capabilities, workflows, and interfaces for simulation, training, evaluation, and deployment.

Defines Niantic Spatial’s embodied AI market narrative, positioning, messaging, campaigns, competitive intelligence, pricing and packaging. Translates technical capabilities into customer proof, sales assets, partner content, and adoption strategies. Collaborates with product, engineering, research, business development, robotics OEMs, and strategic customers to establish the company as a leader in physical AI.