{"id":3050219,"url":"https://alion.io/job/microsoft-senior-applied-scientist-4","title":"Senior 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":81,"open_postings":309,"ghost_share":0,"stale_share":0.767,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-10-24T06:02:35Z"}},"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"],"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":"Diffusion Models","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"LoRA","optional":false},{"name":"Multimodal AI","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"RLAIF","optional":false},{"name":"RLHF","optional":false},{"name":"SFT","optional":false},{"name":"Transformers","optional":false},{"name":"AI Agents","optional":true},{"name":"Machine Learning","optional":true},{"name":"PEFT","optional":true},{"name":"Python","optional":true},{"name":"PyTorch","optional":true}],"status":"live","first_seen_at":"2026-10-20T15:26:54Z","employer_posted_date":"2026-10-21","last_verified_at":"2026-10-24T12:06:20Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"OverviewFrontier Tuning is Microsoft’s AI customization platform that enables enterprises to adapt foundation models to their unique workflows, domains, and data-while preserving security, privacy, and reliability at scale. As we grow, Frontier Tuning is becoming a critical pillar for ensuring enterprise-specific capabilities are systematically learned and reflected across Microsoft 365 and beyond.\nWe are seeking Senior Applied Scientist with solid research and systems-building skills who are excited to push the frontier of large-scale model post-training and adaptation. This role spans algorithmic innovation as well as the design and development of scalable infrastructure and tooling for training, steering, evaluating, and securely deploying enterprise-ready AI systems.\nPost-training may include reinforcement learning, fine-tuning, architectural modification, inference-time control, evaluation-driven adaptation, or privacy-preserving training techniques applied under real-world enterprise deployment constraints.\nWe welcome candidates with experience in one or more of the following areas:\nReinforcement learning or supervised fine-tuning for foundation models.\n\nScalable training systems for RLHF(Reinforcement Learning from Human Feedback)/RLAIF (Reinforcement Learning from AI Feedback) or other post-training pipelines.\n\nTransformer architecture design or efficient adaptation techniques (e.g., LoRA-style methods).\n\nInference-time steering, controllability, or alignment approaches.\n\nDebugging, evaluation, or development tooling for foundation models.\n\nMultimodal model training, including language, vision, or diffusion models.\n\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\nDesign and develop methods to adapt foundation models (e.g., language, diffusion, or multimodal models) for enterprise-specific tasks such as document understanding, workflow automation, or content generation.\n\nContribute to one or more aspects of the post-training stack, including:\n\nReinforcement learning or fine-tuning methods\n\nArchitectural or parameter-efficient adaptation techniques\n\nInference-time steering or controllability approaches\n\nTooling for evaluation, debugging, or model development\n\nHarnesses \nImplement and evaluate adaptation approaches under real-world enterprise deployment constraints such as latency, safety, privacy, policy compliance, and compute efficiency.\n\nPartner with research and engineering teams to translate product or customer requirements into scalable model adaptation solutions.\n\nExplore post-training techniques that improve domain specialization, tool use, planning, or agentic behaviors in enterprise environments.\n\nDrive technical work from concept to prototype, delivering new methods, systems components, or empirical insights that advance enterprise model customization.\n\nDocument approaches and share best practices to improve organizational capabilities in post-training and secure deployment of foundation models.\n\nSupport mentorship and onboarding of interns or early-career team members as appropriate.\n\nQualifications\nRequired Qualifications:\nBachelor'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)OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) \nOR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) 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:\nMicrosoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.\nPreferred Qualifications:\nMaster'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 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\n3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).\nExperience presenting at conferences or other events in the outside research/industry community as an invited speaker.\n3+ years experience conducting research as part of a research program (in academic or industry settings).\n1+ year(s) experience developing and deploying live production systems, as part of a product team.\n1+ year(s) experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.\nExperience contributing to research, open-source systems, or production deployments involving foundation model training or adaptation.\nExperience in one or more of the following areas:\nTransformer or multimodal model architectures.\nReinforcement learning or post-training methods.\nDistributed or large-scale ML training systems.\nEvaluation or benchmarking of AI systems.\nTool use, planning, or agentic model behaviors.\nDeployment of AI solutions in enterprise or customer environments.\nExperience publishing academic papers as a lead author or essential contributor, or contributing to technical work presented at leading conferences in relevant research domains.\n2+ years of experience building scalable ML systems or pipelines for training, adapting, or deploying AI models.\n2+ years of experience with Python and machine learning frameworks (e.g., PyTorch or equivalent).\nReinforcement learning or supervised fine-tuning for foundation models.\nScalable training systems for RLHF/RLAIF or other post-training pipelines.\nTransformer architecture design or efficient adaptation techniques (e.g., LoRA-style methods).\nInference-time steering, controllability, or alignment approaches.\nDebugging, evaluation, or development tooling for foundation models. \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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