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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Machine Learning Engineer - Applied Scientist - **Company:** Microsoft - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $119,800.0 - $234,700.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Microsoft Online Services, Computer Engineering, Machine Learning, Microsoft Software, Software Product Management, Regression Testing, Software Engineering, Evaluation Pipelines, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Information Technology, Low Latency, Machine Learning Operations, Evaluation of Large Language Models - **Published:** November 5, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-machine-learning-engineer-applied-scientist-san-francisco-ca--010ddb63-5f24-4b26-b351-e6087955bc28 ## About the Role Machine Learning, Software Engineering, Machine Learning, Artificial Intelligence (AI), Microsoft Product Family, Continuous Improvement, Product Lifecycle, Product Development, Prototyping, Audiovisual, 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., * Bachelors Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) * OR Masters Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) * OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) * OR equivalent experience. Other Requirements * Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: * Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter., * PhD in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research) * OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) * 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, 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. * #AI #LLM #Evals #AppliedScience Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year. ## Description * 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.