Member of Technical Staff Robotics (Spatial AI)

Microsoft

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: Redmond, WA
Salary: $102,100–$202,200 / yr
Posted: 105 days ago
Freshness: Confirmed live yesterday
Closes: Nov 25, 2026
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $224k

This role $152k

$81k most similar roles pay here $299k

This role pays less than 92% of similar roles. Most pay $193,000–$255,000 — the shaded band above. At the midpoint, this role pays about $152k versus about $224k for comparable roles.

Based on 240 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 598 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 586 roles with salary data.

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At a glance

TL;DR · Member of Technical Staff Robotics (Spatial AI)

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Member of Technical Staff, Microsoft Robotics (Spatial AI) joins the Microsoft Robotics team within the Discovery and Quantum division to develop physical world models for robotic applications. The role involves designing, developing, and testing models that capture 3D spatial structures, object geometry, physics dynamics, and scene semantics to provide robots with the intuition needed for navigation and interaction planning. Key responsibilities include building video prediction models, neural physics simulators, and 3D generative models while creating data pipelines for multi-sensor fusion involving RGB, depth, and LiDAR. The candidate will utilize Python, PyTorch, JAX, or TensorFlow to build systems for scene reconstruction and occupancy prediction. This position addresses the challenge of moving robotics from experimentation to reliable deployment by building a platform for physical intelligence where robots can understand and predict how their environment changes in response to actions.

Skills

Spatial AI World Models 3D Computer Vision PyTorch JAX TensorFlow Python Azure Machine Learning Kubernetes LiDAR Multi-sensor Fusion Model-Predictive Control Reinforcement Learning Scene Graph Construction NeRF Gaussian Splatting GPU Computing Data Pipelines

What you'll do

Design and develop physical world models that capture 3D spatial structures, object geometry, and physics dynamics for robotic applications.
Build and train world models such as video prediction models and neural physics simulators to predict future states of environments.
Develop spatial AI capabilities including 3D scene reconstruction, object detection, and pose estimation for robot perception.
Implement evaluation frameworks to measure prediction accuracy, planning performance, and model generalization across diverse environments.
Build data pipelines for training world models using multi-sensor fusion from RGB, depth, LiDAR, and proprioception data.
Write efficient Python code using PyTorch, JAX, or TensorFlow to develop and train large-scale models on GPU infrastructure.
Contribute to the team's research roadmap by identifying high-impact technical directions for spatial AI and world modeling.
Present research findings and model evaluation results to internal stakeholders and through technical publications or conference presentations.

What we're looking for

A Bachelor's degree in a quantitative field and 2+ years of data science experience is required.
A Master's degree in a quantitative field and 1+ year of data science or consulting experience is required.
A Doctorate in a quantitative field or equivalent professional experience is accepted as a qualification.
Ability to pass the Microsoft Cloud Background Check and other applicable security screenings.
Experience with world models, video prediction models, neural physics simulators, or generative 3D models.
Strong background in 3D computer vision including depth estimation, reconstruction, NeRF/Gaussian splatting, and point cloud processing.
Proficiency in PyTorch, JAX, or TensorFlow for training large-scale models on GPU clusters.
Experience with robotics perception systems, multi-sensor fusion (RGB-D, LiDAR), and object pose estimation.

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