Senior Machine Learning Engineer - Applied Scientist

Microsoft•Redmond, WA

2d•$119,800 - $234,700

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 deliver high-quality AI solutions. The successful candidate will shape how AI capabilities are developed, measured, improved, and brought into production, and will help define customer and business outcomes and rigorous evaluation frameworks that support confident product iteration. The position leads applied science strategy from opportunity identification through experimentation, launch, and ongoing improvement. It establishes success criteria and evaluation methods for LLM-based, agentic, retrieval-augmented, and multimodal systems, and partners with product and engineering leaders to align science priorities with customer needs, product strategy, and execution. It also supports the reliability, scalability, latency, observability, and maintainability of production AI systems; mentors colleagues; influences cross-team practices; and communicates findings and recommendations to technical and non-technical stakeholders. Applicants must meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter. The position is listed as Applied Sciences IC4. The typical U.S. base pay range is $119,800–$234,700 per year; a different range applies in the San Francisco Bay Area and New York City metropolitan area: $160,200–$261,000 per year. Certain roles may be eligible for benefits and other compensation. The position will be open for at least 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 passing 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.
Solid 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 measures of success 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.
Collaborate with software engineering teams to build AI systems with attention to reliability, scalability, latency, observability, maintainability, and MLOps best practices.
Drive rapid iteration by forming hypotheses, defining metrics, running experiments, interpreting results, and recommending product or technical actions.
Establish AI launch quality bars and decision frameworks, including evaluation scorecards, regression testing, guardrail metrics, and responsible AI considerations.
Mentor scientists and engineers and influence cross-team best practices for AI development, experimentation, and evaluation.
Communicate findings, tradeoffs, risks, and recommendations to technical and non-technical stakeholders.