Senior Machine Learning Engineer - Applied Scientist

Microsoft•Redmond, WA

2d•$119,800 - $261,000

About The Position

Microsoft is seeking a Senior Machine Learning Engineer - Applied Scientist to define, evaluate, and build AI-powered product experiences. The role combines applied science expertise in large language models, evaluation systems, AI, MLOps, experimentation, and software engineering with product judgment and the ability to ship high-quality AI solutions. The position shapes how AI capabilities are developed, measured, improved, and brought into production, including defining customer and business success and creating rigorous evaluation frameworks for product iteration. The role leads applied science strategy from identifying opportunities through experimentation, launch, and continuous improvement. It works closely with engineering and product management leadership to align scientific priorities with product strategy, customer needs, roadmaps, and execution. It also mentors scientists and engineers, influences cross-team practices, and communicates findings, tradeoffs, risks, and recommendations to technical and non-technical stakeholders. The position requires the ability to meet Microsoft, customer, and/or government security screening requirements, including passing a Microsoft Cloud Background Check upon hire or transfer and every two years thereafter. The typical U.S. base pay range is USD $119,800–$234,700 per year. For specific work locations in the San Francisco Bay Area and New York City metropolitan area, the range is USD $160,200–$261,000 per year. Certain roles may be eligible for benefits and other compensation. The position will be open for a minimum of five days, with applications accepted on an ongoing basis until filled.

Requirements

Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 6+ years of related experience, such as statistics, predictive analytics, or research; or a master's degree in one of these fields and 4+ years of related experience; or a doctorate in one of these fields and 3+ years of related experience; or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

PhD in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 6+ years of related experience; or a doctorate in one of these fields and 3+ years of related experience; or equivalent experience.
Significant experience building, evaluating, and shipping AI/ML-powered products or services.
Hands-on experience with large language models, including prompt engineering, retrieval-augmented generation (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.
Understanding of product success metrics and ability to translate customer and business goals into measurable scientific and technical objectives.
Software engineering skills and experience collaborating with engineers to build production-quality AI systems.

Responsibilities

Lead applied science strategy for AI-powered product areas, from identifying ambiguous opportunities through experimentation, launch, and continuous improvement.
Define success measures for AI product experiences, including customer value, quality thresholds, reliability, safety, engagement, and measurable business impact.
Design and operationalize LLM evaluation frameworks across prototyping, model and prompt iteration, feature development, pre-launch validation, launch readiness, and post-launch monitoring.
Develop 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 offline experimentation and evaluation pipelines to compare models, prompts, system designs, retrieval strategies, grounding quality, tool use, and end-to-end product behavior.
Partner with engineering and product management leadership to align science priorities with product strategy, roadmap decisions, customer needs, and execution plans.
Work with software engineering teams to build AI systems with attention to reliability, scalability, latency, observability, maintainability, and MLOps best practices.
Drive iteration by forming hypotheses, defining metrics, running experiments, interpreting results, and recommending product or technical actions.
Establish AI launch quality bars and decision-making frameworks, 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 to technical and non-technical stakeholders.