Principal Applied Scientist

Microsoft • Redmond, WA

3d • $142,800 - $304,200

About The Position

We are looking for a Principal Applied Scientist to help define, evaluate, and build the next generation of AI-powered product experiences. This role requires a leader who combines deep applied science expertise in LLMs, evaluation systems, AI, MLOps, experimentation, and software engineering with strong product judgment and a proven ability to ship high-quality AI solutions. You will play a critical role in shaping how AI capabilities are developed, measured, improved, and brought into production. The ideal candidate has experience building AI-based products end to end, defining what success looks like for customers and the business, and creating rigorous evaluation frameworks that guide fast, high-confidence product iteration. 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.

Requirements

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research)
• Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
• equivalent experience.
• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role.
• Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

• Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 13+ years related experience (e.g., statistics, predictive analytics, research)
• Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 10+ years related experience (e.g., statistics, predictive analytics, research)
• equivalent experience.
• 5+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
• 2+ years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
• 5+ years experience conducting research as part of a research program (in academic or industry settings).
• 3+ years experience developing and deploying live production systems, as part of a product team.
• 3+ years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
• Significant experience building, evaluating, and shipping AI/ML-powered products or services.
• Hands-on experience with large language models, including prompt engineering, RAG, agents, tool use, model comparison, failure analysis, and evaluation-driven iteration.
• Deep understanding of LLM evaluation throughout the development lifecycle, including dataset creation, annotation strategies, automated and human evaluation, benchmark design, regression testing, launch criteria, and production monitoring.
• Experience designing offline experiments and interpreting results to inform model, system, and product decisions.
• Experience evaluating multimodal AI systems or AI experiences involving multiple input/output modalities.
• Solid understanding of product success metrics and ability to translate customer/business goals into measurable scientific and technical objectives.
• Solid software engineering skills and experience collaborating with engineers to build production-quality AI systems.
• Experience with MLOps, experimentation infrastructure, telemetry, monitoring, data pipelines, and continuous improvement loops.
• Demonstrated ability to work closely with PM and engineering leadership to influence product direction and drive execution in ambiguous environments.
• Excellent communication, collaboration, and technical leadership skills.
• Experience with agentic workflows, enterprise AI, productivity tools, developer tools, or customer-facing AI products.
• Experience with responsible AI, safety evaluations, red teaming, hallucination measurement, grounding quality, bias/fairness assessment, privacy, or compliance-related AI evaluation.
• Experience creating reusable evaluation platforms, experimentation frameworks, model scorecards, or AI quality dashboards.
• Experience combining offline evaluation, online experimentation, telemetry, and qualitative customer feedback into a unified product decision framework.
• Track record of influencing senior stakeholders through applied science insights and measurable customer or business impact.
• Publications, patents, open-source contributions, or recognized technical leadership in AI/ML are a plus.

Responsibilities

• Lead applied science strategy for AI-powered product areas, from ambiguous opportunity identification through experimentation, launch, and continuous improvement.
• Define what success looks like for AI product experiences, including customer value, quality thresholds, reliability, safety, engagement, and measurable business impact.
• Design and operationalize LLM evaluation frameworks across the product development lifecycle, including early prototyping, model/prompt iteration, feature development, pre-launch validation, launch readiness, and post-launch monitoring.
• Build evaluation methodologies for LLM-based, agentic, retrieval-augmented, and multimodal AI systems, including text, image, document, audio, video, and interaction-based experiences where applicable.
• Develop robust offline experimentation and evaluation pipelines to compare models, prompts, system designs, retrieval strategies, grounding quality, tool use, and end-to-end product behavior.
• Partner closely with engineering and PM leadership to align science priorities with product strategy, roadmap decisions, customer needs, and execution plans.
• Work with software engineering teams to productionize AI systems with strong attention to reliability, scalability, latency, observability, maintainability, and MLOps best practices.
• Drive fast iteration cycles by forming hypotheses, defining metrics, running experiments, interpreting results, and recommending clear product or technical actions.
• Establish quality bars and decision-making frameworks for AI launches, including evaluation scorecards, regression testing, guardrail metrics, and responsible AI considerations.
• Mentor scientists and engineers, raise the technical bar, and influence cross-team best practices for AI development, experimentation, and evaluation.
• Communicate findings, tradeoffs, risks, and recommendations clearly to technical and non-technical stakeholders.

Benefits

• 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, read more about requesting accommodations.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Education Level: No Education Listed