Staff Engineer - Robot Learning, Embodied AI

Niantic Spatial•San Francisco, CA

1d•Hybrid

About The Position

Niantic Spatial is building the future of physical AI with groundbreaking mapping technology that unlocks spatial intelligence for humans and machines. Our technology captures environments with geometric accuracy and detail, and our Visual Positioning System provides precise positioning globally. We serve customers in robotics, public sector, and industrial markets. This role focuses on the real-to-sim product within the Embodied AI group, addressing the fundamental sim-to-real gap in visual-spatial understanding for autonomy teams. The Staff Engineer will lead engineering for this product, inform the roadmap, define technical requirements, and prototype solutions. This is a hands-on, entrepreneurial role for someone with a co-founder mentality who is passionate about building and enabling others.

Requirements

Experience training, evaluating, and deploying robot policies in systems used by enterprise or government customers, with responsibility for performance in real operating conditions.
Deep practical experience using simulation for policy training or evaluation, including tools such as NVIDIA Isaac Sim, Isaac Lab, MuJoCo, or equivalent.
Diagnosed sim-to-real failures and understand when greater fidelity improves outcomes.
Hands-on experience with reinforcement learning, imitation learning, or both, and with deep-learning frameworks such as PyTorch.
A practical understanding of how visual perception and 3D scene representations affect robotics training, evaluation, and real-world performance.
Experience running experiments on real robots, from an ambiguous hypothesis through to defensible results.
Strong Python and C++ skills, with experience building reliable production software.
A bachelor's degree or higher in a relevant field.

Nice To Haves

Authored or maintained a public robotics benchmark.
Helped take an early product to repeatable adoption across multiple customers.
Built or substantially extended robotics simulation infrastructure, including scene authoring, domain randomization, or large-scale training and evaluation.
Trained and deployed vision-based navigation or mobility policies, particularly in varied, unstructured environments.
Built pipelines that turn real-world reconstructions into simulation-ready environments.

Responsibilities

Sharpen the Roadmap: Bring first-hand knowledge of customer pain, existing solutions, and the value of doing better to roadmap decisions, and help focus effort on the most consequential problems to solve.
Own the Technical Requirements: Guide the team to design the real-to-sim stack pragmatically. Make the final recommendation on what to build, integrate, and simplify, ensuring capabilities work within customers' existing training and evaluation workflows.
Benchmark the Impact: Working with our Real-World Test Lab, design and run reproducible evaluations that measure the impact of our real-to-sim stack on policy transfer and real-world performance against customers' existing approaches.
Build and Ship: Lead engineering delivery and personally build critical features. Keep scope tight, remove blockers, and get performant capabilities into customers' hands quickly.
Raise the Technical Bar: Set a high bar for the team's work and deepen its understanding of robotics development and deployment, driving incremental improvement every week.