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About This Role
Overview
Microsoft’s Discovery and Quantum (MDQ) division develops and delivers advanced artificial intelligence (AI), cloud\-enabled capabilities, and strategic technologies to help solve the world’s major challenges. From accelerating scientific discovery with advanced AI tools, to pioneering breakthroughs in quantum computing, to advancing robotics and AI capabilities that drive real\-world impact, joining MDQ means building the future, partnering with fast\-moving innovators, and operating in a high\-impact, mission\-driven environment.
At Microsoft Robotics within MDQ, we build and deploy technologies that enable people, robots, and AI agents to collaborate and achieve more.
We are building Microsoft’s platform for physical intelligence—an integrated robotics software and AI platform that brings together humans, robots, and agents through robotics AI models, innovative teaming solutions and experiences, physically grounded agentic AI workflows, trustworthy test and evaluation, and real\-world customer\-focused validation. Built on Microsoft’s core platforms and delivered through and with a global ecosystem of partners and customers, this platform accelerates AI for the physical world and helps robotics solutions move from experimentation to reliable, scaled deployment.
We are hiring a Member of Technical Staff, Microsoft Robotics (Spatial AI) at the data \& applied science II level, to design, develop, and test physical world models that enable robots to understand, predict, and reason about the 3D physical environments in which they operate. This role focuses on building models that capture spatial structure, object relationships, physics dynamics, and scene semantics, providing robots with the physical intuition needed for manipulation, navigation, and interaction planning. The engineer will work at the frontier of world modeling, spatial AI, and foundation models for robotics, contributing to models that predict how the physical world changes in response to robot actions and environmental dynamics.
Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.
\#MicrosoftRobotics \#MDQ
Responsibilities
- Design, develop, and evaluate physical world models that capture 3D spatial structure, object geometry and pose, physics dynamics, material properties, and semantic scene understanding for robotic applications.
- Build and train world models (e.g., video prediction models, neural physics simulators, 3D generative models, scene graph representations) that predict future states of physical environments conditioned on robot actions, enabling model\-based planning and policy learning.
- Develop spatial AI capabilities including 3D scene reconstruction, object detection and pose estimation, spatial relationship reasoning, occupancy prediction, and dense 3D feature representations for robot perception and planning.
- Implement and maintain evaluation frameworks for world models and spatial AI systems, including prediction accuracy metrics, planning performance benchmarks, and generalization testing across environments and object categories.
- Collaborate with robotics researchers, learning engineers, and simulation engineers to integrate world models into robot planning and control pipelines, enabling model\-predictive control, imagination\-based planning, and data\-augmented training.
- Build data pipelines for training world models, including multi\-sensor data fusion (RGB, depth, LiDAR, proprioception), scene annotation, and dataset curation for diverse physical environments and interaction scenarios.
- Write efficient, readable, extensible code in Python (including PyTorch, JAX, or TensorFlow) for model development, training, and evaluation, leveraging GPU computing infrastructure for large\-scale experiments.
- Contribute to the formulation of the team’s world modeling research and development roadmap, identifying high\-impact technical directions and collaborating with leadership to prioritize investments.
- Present research findings and model evaluation results clearly and efficiently to internal stakeholders and external partners, contributing to technical publications, blog posts, and conference presentations.
- Stay current with state\-of\-the\-art research in world models, spatial AI, 3D vision, neural physics simulation, and foundation models for physical understanding, actively contributing to the body of thought leadership in these areas.
Qualifications *Required Qualifications:*
- Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2\+ years data\-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
- + OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1\+ year(s) data\-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) or consulting experience
+ OR equivalent experience.
*Other Requirements:** Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to the following specialized security screenings
+ Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.
*Preferred Qualifications:*
- Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5\+ years data\-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
- + OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3\+ years data\-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
+ OR Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1\+ year(s) data\-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results)
+ OR equivalent experience.
- Strong background in robotics perception, navigation, and proprioceptive sensor stacks and systems integration, including algorithm and model development and implementation in real\-world applications.
- Experience with world models, video prediction models, neural physics simulators, or generative 3D models for physical environment understanding and prediction.
- Strong background in 3D computer vision, including depth estimation, 3D reconstruction, NeRF/Gaussian splatting, point cloud processing, or spatial reasoning.
- Proficiency in PyTorch, JAX, or TensorFlow with experience training large\-scale models on GPU clusters (Azure Machine Learning, Kubernetes, or equivalent).
- Experience with robotics perception systems, including multi\-sensor fusion (RGB\-D, LiDAR, proprioception), object pose estimation, or scene graph construction.
- Familiarity with model\-based reinforcement learning, model\-predictive control, or imagination\-based planning approaches that leverage learned world models.
- Published research or demonstrated contributions to world models, spatial AI, 3D vision, neural simulation, or physical AI in top\-tier venues (NeurIPS, ICML, ICLR, CoRL, RSS, CVPR, ECCV, or equivalent).
- Experience with the Microsoft Azure AI toolset (Azure Machine Learning, Azure Databricks, Azure Cognitive Services) or equivalent cloud ML platforms.
Data Science IC3 \- The typical base pay range for this role across the U.S. is USD $102,100\.00 \- $202,200\.00 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $133,800\.00 \- $219,200\.00 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us\-corporate\-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process.
Salary Context
This $102K-$219K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Microsoft, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($160K) sits 10% below the category median. Disclosed range: $102K to $219K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Microsoft AI Hiring
Microsoft has 17 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Product Manager, AI Software Engineer. Positions span Redmond, WA, US, Mountain View, CA, US, Dallas, TX, US. Compensation range: $219K - $304K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (119) are outnumbered by mid-level (1,813) and senior (1,472) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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