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Overview
Microsoft is committed to ensuring that we develop and deploy our AI technologies in ways that uphold our AI principles and warrant people’s trust. The Office of Responsible AI plays a leading role in this effort as part of Microsoft’s Corporate, External, and Legal Affairs (CELA) division. With a cross-company mandate, we establish internal policy for how our engineering and sales teams implement responsible AI practices, enable our community of Responsible AI Champs to support our responsible AI program, and review engineering and customer projects that require additional input based on potential impact. We also help to shape new laws, norms, and standards to advance responsible AI for the benefit of society at large.
The Sensitive Uses and Emerging Technologies team within the Office of Responsible AI is responsible for providing guidance on Microsoft’s most sensitive and cutting-edge AI products, emerging technologies, and initiatives. Across Microsoft, we work alongside engineering colleagues on AI model and product development and engage with Microsoft researchers and sales teams from around the world. Within the Office of Responsible AI, we partner closely with our Internal Policy & Practice team, whose remit includes developing responsible AI policies and practices that scale across the company and supporting the implementation efforts for our Responsible AI Standard.
As a Principal Technical Program Manager, Responsible AI on the Sensitive Uses and Emerging Technologies team, you will provide internal consulting and assessment of high-impact AI use cases and support all facets of the Sensitive Uses program. You will evaluate AI-driven products to assess risks, define requirements, develop strategic initiatives, manage sensitive use case management programs and processes, support incident response processes, and socialize policies that support Microsoft’s ability to develop and deploy AI systems safely and responsibly. In doing so, you will partner with engineering, policy, research, sales, and marketing, stakeholders to rapidly adjust to the changing AI and policy landscape, drive new requirements across product teams, and cutting-edge AI technology at the forefront of a new computing paradigm.
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.
Responsibilities
- Support the Sensitive Uses and Emerging Technologies team within the Office of Responsible AI to evaluate and develop internal guidance and programs for Microsoft’s highest-impact AI models, products, and use cases from across Microsoft, including model and product releases using generative AI and state-of-the-art frontier models.
- Develop insights, recognize patterns, and assess AI system architectures with an eye toward providing actionable risk mitigation guidance. Use your deep knowledge of use cases and product deployments to develop more generalizable, scalable policies to account for the risks and impacts of AI systems deployed in an increasingly wide range of social contexts.
- Partner with engineering teams to contribute to, prioritize, and deliver on product roadmaps, primarily by evaluating their upcoming product releases for responsible AI risks and evidence of mitigation effectiveness. Develop a deep understanding of AI technology and responsible AI engineering implementations to connect to and develop AI policy.
- Manage, document, and drive progress to uphold responsible AI requirements across a diverse group of Microsoft engineering, sales, and research teams implementing AI systems in products, features, and customer engagements. Think through global challenges and drive accountability and results through others.
- Manage governance programs and processes for AI systems to ensure that specific performance requirements and standards are met throughout the development lifecycle.
- Create relationships to drive orchestration and integration efforts for the development and implementation process of responsible AI for multiple feature areas (e.g., product, services) and large and complex cross-functional projects with internal teams. Collaborates and coordinates across organizations to ensure alignment on product management and release, including tradeoffs, adjustments, and improvements as feasible.
- Embody Microsoft’s culture and a demonstrated commitment to the company’s core values, including a commitment to high ethical standards, unquestioned integrity, and a commitment to advancing the company’s diversity & inclusion commitments.
Qualifications Required Qualifications
- Bachelor's Degree AND 6+ years experience in engineering, product/technical program management, data analysis, applied science, technology policy, research, governance, product development; or related area + OR equivalent experience.
- 3+ years experience managing cross-functional and/or cross-team projects.
Preferred Qualifications
- 3+ years work experience on issues pertaining to artificial intelligence, machine learning, or responsible AI, such as AI/ML engineering, AI-related technical program management, AI product development, data science with AI/ML components, inclusive design, ethics, safety, fairness, explainability, accessibility, privacy, security, AI governance, and/or AI policy.
- Experience with generative AI a plus but not required.
- Specific technical experience in fields of responsible AI, AI ethics, AI safety, digital safety, or security a plus but not required.
- Experience managing cross-functional and/or cross-team projects.
- Ability to navigate ambiguity and adapt to rapidly changing environments and demands. When dealing with emergent and complex topics, you strive to create structure and process that will support progress while remaining flexible as positions evolve.
- Experience working in or with a large, matrixed organization.
- Proven record of being an effective team player dedicated to collective successes. The ability to promote cooperation and solve problems in a timely, creative, and professional manner. Proficiency at working closely and collaboratively with multiple departments and executives, and in cross-functional teams.
- A proven track record of being able to engage and influence a variety of stakeholders, both within and outside Microsoft. Ability to collect and reason through multi-stakeholder perspectives, including those of executives, engineers and developers, sellers, researchers, civil society organizations, and regulators, all of whom grapple with new and complex technologies and their implications for society.
- Highly developed written and verbal communication skills. Ability to proactively advance company positions through high-impact engagement in small and/or large group settings, including communications with business leaders.
- Experience in strategic program management, execution, operations, and evaluation. Demonstrated ability to manage multiple priorities, projects, and deadlines simultaneously. You can quickly spot technical and process blockers towards achieving outcomes and work collaboratively to develop strategies to overcome them.
- Proven record of experimentation and openness to feedback, learning from both successes and failures.
Technical Program Management IC5 - The typical base pay range for this role across the U.S. is USD $142,800 - $274,800 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 $188,000 - $304,200 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 $142K-$304K range is below the median for AI Safety roles in our dataset (median: $230K across 13 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 10,827 AI roles we're tracking, AI Safety positions make up 0% of the market. At Microsoft, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills in Demand for This Role
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Safety roles pay a median of $266,050 based on 70 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($223K) sits 16% below the category median. Disclosed range: $142K to $304K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Engineering Manager ($253,461) and Research Engineer ($252,000). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
Microsoft AI Hiring
Microsoft has 78 open AI roles right now. They're hiring across AI/ML Engineer, Data Engineer, AI Product Manager, Research Scientist. Positions span Mountain View, CA, Singapore, Singapore, Remote, US. Compensation range: $108K - $331K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 median).
Career Path
Common paths into AI Safety roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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