Senior Machine Learning Engineer - Applied Scientist

Senior Machine Learning Engineer - Applied Scientist

United States, Washington, Redmond

Job description

Company and benefits

Job number

200062501

Date posted

Oct 19, 2026

Work site

3 days / week in-office

Travel

Less than 25%

Profession

Research, Applied, & Data Sciences

Discipline

Applied Sciences

Role type

Individual Contributor

Employment type

Full-Time

Overview

We are looking for a Senior Machine Learning Engineer - 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.

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 produce 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.

Qualifications

• Bachelor's Degree in Statistics, Econometr