{"id":3651655,"url":"https://alion.io/job/microsoft-senior-machine-learning-engineer-applied-scientist-2","title":"Senior Machine Learning Engineer - Applied Scientist","company":{"id":18,"name":"Microsoft","domain":"microsoft.com","url":"https://alion.io/company/microsoft","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"B","score":84,"open_postings":423,"ghost_share":0.005,"stale_share":0.605,"repost_share":0.028,"time_to_fill_p50_days":29,"computed_at":"2026-11-05T05:54:30Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Washington, United States","Mountain View, United States","New York, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":119800,"max":234700,"currency":"USD","period":"year","gross":null,"usd_annual":234700},"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"RAG","optional":false},{"name":"Prompt Engineering","optional":true}],"status":"live","first_seen_at":"2026-10-27T19:32:27Z","employer_posted_date":"2026-11-02","last_verified_at":"2026-11-05T21:04:19Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"OverviewWe 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.\nYou 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.\nMicrosoft’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.\nResponsibilities\nLead applied science strategy for AI-powered product areas, from ambiguous opportunity identification through experimentation, launch, and continuous improvement.\nDefine what success looks like for AI product experiences, including customer value, quality thresholds, reliability, safety, engagement, and measurable business impact.\nDesign 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.\nBuild evaluation methodologies for LLM-based, agentic, retrieval-augmented, and multimodal AI systems, including text, image, document, audio, video, and interaction-based experiences where applicable.\nDevelop robust offline experimentation and evaluation pipelines to compare models, prompts, system designs, retrieval strategies, grounding quality, tool use, and end-to-end product behavior.\nPartner closely with engineering and PM leadership to align science priorities with product strategy, roadmap decisions, customer needs, and execution plans.\nWork with software engineering teams to produce AI systems with strong attention to reliability, scalability, latency, observability, maintainability, and MLOps best practices.\nDrive fast iteration cycles by forming hypotheses, defining metrics, running experiments, interpreting results, and recommending clear product or technical actions.\nEstablish quality bars and decision-making frameworks for AI launches, including evaluation scorecards, regression testing, guardrail metrics, and responsible AI considerations.\nMentor scientists and engineers, raise the technical bar, and influence cross-team best practices for AI development, experimentation, and evaluation.\nCommunicate findings, tradeoffs, risks, and recommendations clearly to technical and non-technical stakeholders.\nQualifications\nBachelor's 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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics, predictive analytics, research) \nOR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)\nOR equivalent experience.\n\nOther Requirements\nAbility 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.\n\nPreferred Qualifications\nPhD 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)\nOR equivalent experience.\n\nSignificant experience building, evaluating, and shipping AI/ML-powered products or services.\nHands-on experience with large language models, including prompt engineering, RAG, agents, tool use, model comparison, failure analysis, and evaluation-driven iteration.\nDeep 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.\nExperience designing offline experiments and interpreting results to inform model, system, and product decisions.\nExperience evaluating multimodal AI systems or AI experiences involving multiple input/output modalities.\nSolid understanding of product success metrics and ability to translate customer/business goals into measurable scientific and technical objectives.\nSolid software engineering skills and experience collaborating with engineers to build production-quality AI systems.\n\n#AI #LLM #Evals #AppliedScience\nApplied 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.\nCertain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:\nhttps://careers.microsoft.com/us/en/us-corporate-pay\nThis position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.\nMicrosoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. 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