At Microsoft AI, we are on a mission to train the world’s most capable AI frontier models, pushing the boundaries of scale, performance, and product deployment. Our vision is to pursue humanist superintelligence, that is designed to remain controllable, aligned, and firmly in service to humanity. 

The MAI Futures team is hiring a Member of the Futures Team (Frontier Risk & Alignment) to help shape and realize that vision. The team researches, informs and prepares Microsoft AI and the broader ecosystem for the future of AI and society.  

Reporting into the Frontier Risk & Alignment sub-team, you will lead research that anticipates risks of advanced AI systems, translate it into publications, and inform Microsoft’s technical and operational workstreams. The topics addressed by the team include superintelligence preparedness, multi-agent safety, traditional frontier risk vectors, research underpinning humanist AI model alignment and more. 

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. 

MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction. 

• Doctoral degree in a related field, OR another advanced degree with equivalent research experience. 

• Research experience in one or more of:  

• Frontier AI safety or governance;  

• Agentic AI safety, collaboration, or societal implications;  

• Alignment;  

• Philosophical and ethical aspects of AI;  

• Institutional preparedness for advanced AI. 

• Deep expertise in frontier risk vectors, frameworks, regulation, or evaluation and mitigation pathways. 

• Track record of writing and thought leadership across formats, including academic papers, books or chapters, journalism, or other public-facing work on AI and society. 

• Sufficient technical understanding of AI, and a proven ability to collaborate with technical stakeholders, anticipate risks grounded in technical developments, and inform mitigation efforts. 

• Strong verbal and written communication skills. 

Additional or Preferred Qualifications (PQs) 

• Peer-reviewed publications in leading venues. 

• Experience with frontier safety governance or research in leading labs, think tanks, or academic institutions. 

• Applied experience developing or informing alignment frameworks, model specs, novel evaluations, or technical mitigations. 

• Research spanning both empirical and conceptual work on the future impact of AI. 

At Microsoft AI, we are on a mission to train the world’s most capable AI frontier models, pushing the boundaries of scale, performance, and product deployment. Our vision is to pursue humanist superintelligence, that is designed to remain controllable, aligned, and firmly in service to humanity. 

The MAI Futures team is hiring a Member of the Futures Team (Frontier Risk & Alignment) to help shape and realize that vision. The team researches, informs and prepares Microsoft AI and the broader ecosystem for the future of AI and society.  

Reporting into the Frontier Risk & Alignment sub-team, you will lead research that anticipates risks of advanced AI systems, translate it into publications, and inform Microsoft’s technical and operational workstreams. The topics addressed by the team include superintelligence preparedness, multi-agent safety, traditional frontier risk vectors, research underpinning humanist AI model alignment and more. 

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. 

MAI employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles (U.S.) or 25 miles (non-U.S., country-specific) of that location. This expectation is subject to local law and may vary by jurisdiction. 

• Doctoral degree in a related field, OR another advanced degree with equivalent research experience. 

• Research experience in one or more of:  

• Frontier AI safety or governance;  

• Agentic AI safety, collaboration, or societal implications;  

• Alignment;  

• Philosophical and ethical aspects of AI;  

• Institutional preparedness for advanced AI. 

• Deep expertise in frontier risk vectors, frameworks, regulation, or evaluation and mitigation pathways. 

• Track record of writing and thought leadership across formats, including academic papers, books or chapters, journalism, or other public-facing work on AI and society. 

• Sufficient technical understanding of AI, and a proven ability to collaborate with technical stakeholders, anticipate risks grounded in technical developments, and inform mitigation efforts. 

• Strong verbal and written communication skills. 

Additional or Preferred Qualifications (PQs) 

• Peer-reviewed publications in leading venues. 

• Experience with frontier safety governance or research in leading labs, think tanks, or academic institutions. 

• Applied experience developing or informing alignment frameworks, model specs, novel evaluations, or technical mitigations. 

• Research spanning both empirical and conceptual work on the future impact of AI. 

Microsoft AI (MAI) is building consumer health experiences – including Copilot Health – that bring trustworthy, AI-powered guidance to millions of people. Health information is among the most sensitive data anyone shares, and the trust people place in us depends on getting privacy, security, and regulatory compliance right from the very first line of code. 

We are looking to hire our engineering team’s anchor for all things privacy, compliance, and regulatory. This is a senior individual-contributor role for a hands-on engineer who can set technical direction, build privacy-preserving systems, and serve as the trusted point of contact who keeps our products on the right side of a fast-evolving regulatory landscape. You will work at the intersection of privacy engineering, health technology, and applied AI – partnering closely with product, security, legal, and compliance to make privacy-by-design the default rather than an afterthought. 

If you are energized by hard problems where protecting people and shipping great products are equally non-negotiable, this role offers outsized impact on how responsible AI in health is built. 

• Be the primary point of contact for privacy, compliance, and regulatory matters within the engineering team – the person product and engineering turn to for guidance, design review, and decisions. 

• Ensure products such as Copilot Health are designed and built in a privacy-preserving way, embedding data minimization, purpose limitation, and privacy-by-design into architecture from the outset. 

• Translate complex health and data-protection regulations – including UK and EU GDPR, HIPAA, and the EU AI Act – into concrete technical requirements, engineering guardrails, and automated, continuously verifiable controls. 

• Lead privacy and security design reviews and threat modeling for new features and models, identifying risks early and architecting practical, scalable mitigations. 

• Design and build foundational privacy infrastructure: data classification, policy-driven access controls, consent and preference management, audit logging, data lineage, and retention and lifecycle controls. 

• Evaluate and apply privacy-enhancing technologies – such as differential privacy, de-identification, secure enclaves, and federated approaches – where they meaningfully reduce risk to users. 

• Build reusable libraries, patterns, and tooling that let every engineer ship privacy-preserving features by default, and raise the team’s privacy and compliance fluency through mentorship and clear standards. 

• Partner across legal, compliance, security, and product to balance strong user protections with product velocity, and to support audits, certifications, and regulator-facing evidence. 

• Stay ahead of emerging health-privacy regulation and industry practice, bringing that perspective into roadmaps before requirements become blockers. 

At Microsoft AI, our Health team is on a mission to help millions of users better understand and proactively manage their health and wellbeing. We’re responsible for ensuring that Microsoft AI’s models and services are useful, trusted and safe across diverse customer health journeys. 

What “Applied AI” means at Microsoft AI 

We turn frontier models into products people can trust with their health. We build rigorous, health-specific evals and use them to drive real product decisions. We master orchestration, from harness and context engineering to blending different model classes and families and applying state-of-the-art techniques. And we bring deep, bleeding-edge AI expertise that uplevels the wider team and helps shape the product and engineering roadmap. 

The role 

We are looking for an Applied AI Lead to join our engineering team. This is a hands-on leadership role: you will set the technical direction for this work in the health domain, while growing and developing the engineers who build it. You will be predominantly focused on building Copilot Health, acting as a key bridge between the latest research and product and playing a pivotal role in establishing Copilot as the leader in safe, informative, trustworthy and useful health information. 

You’ll bring very strong proficiency in designing, building and running LLM evaluations, and in LLM orchestration: building agentic, multi-step systems that combine prompting, tool use and retrieval to deliver reliable results in production. 

Lead the team 

• Lead, mentor and grow a team of Applied AI Engineers, fostering a collaborative, inclusive and high-performing environment where engineers do the best work of their careers. 

• Stay deeply hands-on. Set the technical bar through code and design reviews, lead by example on the hardest problems, and remain a credible technical authority on evals and LLM systems. 

• Co-own the roadmap. Partner with product leads to qualify and size new opportunities, co-author the product roadmap, and lead the architecture and development of new products and features from 0 to 1. 

• Own delivery. Plan and prioritise the team’s roadmap, balance a strong bias towards shipping and learning with a high-quality bar, and ensure the reliability of what reaches production. 

Set the technical direction on evaluation and orchestration 

• Define the evaluation strategy. Design and oversee evaluation systems that test LLM capabilities in the health domain, including internal benchmarking and regression testing that capture model accuracy, safety and utility – and make sure results are interpreted and clearly communicated to stakeholders. 

• Architect LLM orchestration. Guide the design of agentic, multi-step systems that combine prompt / context engineering, tool use and retrieval, and champion best practices for building and deploying them reliably at scale. 

• Run and direct experiments to determine how different prompting and orchestration techniques affect results on internal and industry benchmarks, and turn those findings into product improvements. 

• Invest in tooling. Improve the internal tooling used to implement, run and analyse evaluations, and the data pipelines – dataset sourcing, curation and synthesis – that feed them. 

Required 

• Bachelor’s or higher degree in Computer Science or a related technical discipline, AND significant Python programming experience / machine learning research. 

• Very strong proficiency with LLM evaluations – demonstrated experience designing, building and running eval pipelines, curating and synthesising datasets, designing automated analyses, and explaining results to internal stakeholders. 

• Very strong proficiency with LLM orchestration – deep, hands-on experience building with and around LLMs, including prompt / context engineering, tool use, harness engineering, retrieval and agentic, multi-step systems, and building tools to analyse and understand their performance. 

• Proven engineering leadership – 8+ years of software engineering experience, including at least 3 years leading technical teams or projects as a tech lead and/or people manager – mentoring and developing engineers, and guiding a group to deliver high-quality results. Formal people-management experience is preferred; a strong technical lead ready to step into a TLM role will be considered. 

• 0-to-1 experience with a bias towards shipping and learning while balancing a high-quality bar. 

• Experience collaborating in cross-functional teams, working through ambiguity to deliver high-quality results, and a proven ability to contribute to a positive, inclusive work environment that fosters knowledge sharing and growth. 

Preferred 

• Experience in healthcare technology, or experience in the health domain. 

• Experience with data engineering – handling text dataset sourcing, curation and processing tasks at scale. 

• Experience translating cutting-edge research into shipped products in a fast-paced, startup-like environment. 

• Passionate about conversational AI and its deployment. 

• Demonstrated written and verbal communication skills, with the ability to work closely with cross-functional teams including product managers, designers and other engineers. 

• Passion for learning new technologies and staying up to date with industry trends, best practices and emerging patterns in AI. 

At Microsoft AI, our Health team is on a mission to help millions of users better understand and proactively manage their health and wellbeing. We’re responsible for ensuring that Microsoft AI’s models and services are useful, trusted and safe across diverse customer health journeys. 

What “Applied AI” means at Microsoft AI 

We turn frontier models into products people can trust with their health. We build rigorous, health-specific evals and use them to drive real product decisions. We master orchestration, from harness and context engineering to blending different model classes and families and applying state-of-the-art techniques. And we bring deep, bleeding-edge AI expertise that uplevels the wider team and helps shape the product and engineering roadmap. 

The role 

We are looking for an Applied AI Lead to join our engineering team. This is a hands-on leadership role: you will set the technical direction for this work in the health domain, while growing and developing the engineers who build it. You will be predominantly focused on building Copilot Health, acting as a key bridge between the latest research and product and playing a pivotal role in establishing Copilot as the leader in safe, informative, trustworthy and useful health information. 

You’ll bring very strong proficiency in designing, building and running LLM evaluations, and in LLM orchestration: building agentic, multi-step systems that combine prompting, tool use and retrieval to deliver reliable results in production. 

Lead the team 

• Lead, mentor and grow a team of Applied AI Engineers, fostering a collaborative, inclusive and high-performing environment where engineers do the best work of their careers. 

• Stay deeply hands-on. Set the technical bar through code and design reviews, lead by example on the hardest problems, and remain a credible technical authority on evals and LLM systems. 

• Co-own the roadmap. Partner with product leads to qualify and size new opportunities, co-author the product roadmap, and lead the architecture and development of new products and features from 0 to 1. 

• Own delivery. Plan and prioritise the team’s roadmap, balance a strong bias towards shipping and learning with a high-quality bar, and ensure the reliability of what reaches production. 

Set the technical direction on evaluation and orchestration 

• Define the evaluation strategy. Design and oversee evaluation systems that test LLM capabilities in the health domain, including internal benchmarking and regression testing that capture model accuracy, safety and utility – and make sure results are interpreted and clearly communicated to stakeholders. 

• Architect LLM orchestration. Guide the design of agentic, multi-step systems that combine prompt / context engineering, tool use and retrieval, and champion best practices for building and deploying them reliably at scale. 

• Run and direct experiments to determine how different prompting and orchestration techniques affect results on internal and industry benchmarks, and turn those findings into product improvements. 

• Invest in tooling. Improve the internal tooling used to implement, run and analyse evaluations, and the data pipelines – dataset sourcing, curation and synthesis – that feed them. 

Required 

• Bachelor’s or higher degree in Computer Science or a related technical discipline, AND significant Python programming experience / machine learning research. 

• Very strong proficiency with LLM evaluations – demonstrated experience designing, building and running eval pipelines, curating and synthesising datasets, designing automated analyses, and explaining results to internal stakeholders. 

• Very strong proficiency with LLM orchestration – deep, hands-on experience building with and around LLMs, including prompt / context engineering, tool use, harness engineering, retrieval and agentic, multi-step systems, and building tools to analyse and understand their performance. 

• Proven engineering leadership – 8+ years of software engineering experience, including at least 3 years leading technical teams or projects as a tech lead and/or people manager – mentoring and developing engineers, and guiding a group to deliver high-quality results. Formal people-management experience is preferred; a strong technical lead ready to step into a TLM role will be considered. 

• 0-to-1 experience with a bias towards shipping and learning while balancing a high-quality bar. 

• Experience collaborating in cross-functional teams, working through ambiguity to deliver high-quality results, and a proven ability to contribute to a positive, inclusive work environment that fosters knowledge sharing and growth. 

Preferred 

• Experience in healthcare technology, or experience in the health domain. 

• Experience with data engineering – handling text dataset sourcing, curation and processing tasks at scale. 

• Experience translating cutting-edge research into shipped products in a fast-paced, startup-like environment. 

• Passionate about conversational AI and its deployment. 

• Demonstrated written and verbal communication skills, with the ability to work closely with cross-functional teams including product managers, designers and other engineers. 

• Passion for learning new technologies and staying up to date with industry trends, best practices and emerging patterns in AI. 

Microsoft AI (MAI) is building consumer health experiences – including Copilot Health – that bring trustworthy, AI-powered guidance to millions of people. Health information is among the most sensitive data anyone shares, and the trust people place in us depends on getting privacy, security, and regulatory compliance right from the very first line of code. 

We are looking to hire our engineering team’s anchor for all things privacy, compliance, and regulatory. This is a senior individual-contributor role for a hands-on engineer who can set technical direction, build privacy-preserving systems, and serve as the trusted point of contact who keeps our products on the right side of a fast-evolving regulatory landscape. You will work at the intersection of privacy engineering, health technology, and applied AI – partnering closely with product, security, legal, and compliance to make privacy-by-design the default rather than an afterthought. 

If you are energized by hard problems where protecting people and shipping great products are equally non-negotiable, this role offers outsized impact on how responsible AI in health is built. 

• Be the primary point of contact for privacy, compliance, and regulatory matters within the engineering team – the person product and engineering turn to for guidance, design review, and decisions. 

• Ensure products such as Copilot Health are designed and built in a privacy-preserving way, embedding data minimization, purpose limitation, and privacy-by-design into architecture from the outset. 

• Translate complex health and data-protection regulations – including UK and EU GDPR, HIPAA, and the EU AI Act – into concrete technical requirements, engineering guardrails, and automated, continuously verifiable controls. 

• Lead privacy and security design reviews and threat modeling for new features and models, identifying risks early and architecting practical, scalable mitigations. 

• Design and build foundational privacy infrastructure: data classification, policy-driven access controls, consent and preference management, audit logging, data lineage, and retention and lifecycle controls. 

• Evaluate and apply privacy-enhancing technologies – such as differential privacy, de-identification, secure enclaves, and federated approaches – where they meaningfully reduce risk to users. 

• Build reusable libraries, patterns, and tooling that let every engineer ship privacy-preserving features by default, and raise the team’s privacy and compliance fluency through mentorship and clear standards. 

• Partner across legal, compliance, security, and product to balance strong user protections with product velocity, and to support audits, certifications, and regulator-facing evidence. 

• Stay ahead of emerging health-privacy regulation and industry practice, bringing that perspective into roadmaps before requirements become blockers. 

We’re looking for a Senior Applied Scientist with deep expertise in Applied AI, Machine Learning, and Deep Learning, and a proven track record of delivering innovative, user-facing AI experiences at scale. This is a unique opportunity to shape the future of enterprise productivity through cutting-edge Large Language Models (LLMs) and AI-powered experiences in Microsoft 365 Copilot, one of Microsoft’s flagship products. 

You will join a fast-paced, highly collaborative team of scientists, engineers, and product managers working at the forefront of AI innovation. In this role, you will drive the development of advanced LLM-based capabilities, reasoning systems, and real-time orchestration platforms, translating state-of-the-art research into impactful experiences for millions of enterprise users. Your work will directly influence product strategy, model quality, and the next generation of intelligent productivity solutions across Microsoft 365. 

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. 

• Develop scientific approaches for evaluating and improving LLM reasoning, grounding, retrieval and agentic behaviors using offline benchmarking, online experimentation and large-scale telemetry analysis. 

• Drive complex technical investigations, model evaluations and experimentation to improve quality, reliability, latency, cost efficiency and user experience. 

• Define and execute rigorous evaluation methodologies, offline benchmarks, online experimentation frameworks and data-driven approaches for measuring model and product performance. 

• Partner closely with engineering and product teams to translate research innovations into scalable, production-ready systems used by millions of customers. 

• Deeply analyze model behavior, failure modes, grounding effectiveness, reasoning quality, tool use, retrieval performance and orchestration outcomes to identify opportunities for improvement. 

• Develop and advance approaches for agentic workflows, multi-step reasoning, planning, retrieval-augmented generation (RAG), tool orchestration and reinforcement-learning-based optimization. 

• Review technical designs, experimental results and implementation approaches to ensure high standards of scientific rigor, reproducibility and engineering excellence. 

• Collaborate with researchers, applied scientists and engineers to solve challenging technical problems spanning LLMs, machine learning, deep learning, information retrieval and human-AI interaction. 

• Stay current with advances in Large Language Models, reasoning systems, agent architectures, reinforcement learning and applied AI, incorporating relevant innovations into production systems.