Senior Machine Learning Engineer - Applied Scientist
About The Position
Microsoft is seeking a Senior Machine Learning Engineer - Applied Scientist to help define, evaluate, and build AI-powered product experiences. The role combines applied science expertise in LLMs, evaluation systems, AI, MLOps, experimentation, and software engineering with product judgment and the ability to deliver high-quality AI solutions. The position shapes how AI capabilities are developed, measured, improved, and brought into production. It involves building AI products end to end, defining customer and business success, and creating rigorous evaluation frameworks that support rapid, high-confidence iteration. The role also requires meeting Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
Requirements
- Bachelor's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 6+ years of related experience, 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 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 LLMs, including prompt engineering, RAG, agents, tool use, model comparison, failure analysis, and evaluation-driven iteration.
- Deep understanding of LLM evaluation across 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 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 products, from identifying opportunities through experimentation, launch, and continuous improvement.
- Define success measures for AI product experiences, including customer value, quality, reliability, safety, engagement, and business impact.
- Design and operationalize LLM evaluation frameworks throughout product development, from prototyping and iteration to pre-launch validation and post-launch monitoring.
- Develop evaluation methodologies for LLM-based, agentic, retrieval-augmented, and multimodal AI systems and experiences.
- Build 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, roadmaps, customer needs, and execution plans.
- Work with software engineering teams to build reliable, scalable, observable, maintainable AI systems with appropriate latency and MLOps 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 frameworks, including evaluation scorecards, regression testing, guardrail metrics, and responsible AI considerations.
- Mentor scientists and engineers and influence cross-team practices for AI development, experimentation, and evaluation.
- Communicate findings, tradeoffs, risks, and recommendations to technical and non-technical stakeholders.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
