Principal Applied Scientist
 Microsoft | |
 United States, Washington, Redmond | |
 Oct 29, 2025 | |
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OverviewCopilot Discover helps hundreds of millions of people be informed, entertained, and inspired by surfacing highly relevant, trustworthy, and delightful content across Microsoft surfaces. We're building the next generation of AIpowered quality understanding and recommendation systems-spanning text, images, audio, and video-to curate the right content at the right moment while upholding safety and integrity. As a Principal Applied Scientist, you'll lead the science behind Discover's ranking and contentquality stack, combining LLMs, multimodal models, and largescale recommender systems to drive measurable gains in engagement, satisfaction, and trust. You will set technical direction, mentor a highcaliber science cohort, and partner closely with engineering, PM, UXR, and policy to ship endtoend outcomes. You will contribute to the development of the next generation of MSN that is adopting the latest generative AI techniques. 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. Starting January 26, 2026, Microsoft AI (MAI) employees who live within a 50- mile commute of a designated Microsoft office in the U.S. or 25-mile commute of a non-U.S., country-specific location are expected to work from the office at least four days per week. This expectation is subject to local law and may vary by jurisdiction.
ResponsibilitiesLead contentquality understanding at scale. Design and deploy models that assess credibility, usefulness, freshness, safety, and diversity across modalities; reduce misinformation/toxicity error rates through prompt and modellevel innovations; build humanintheloop and activelearning pipelines that get better over time.Advance the recommendation & ranking stack. Architect and productionize largescale DNN/LLMenhanced recommenders (representation learning, sequence modeling, retrieval/ranking, slate optimization), balancing user satisfaction, content quality, and business goals.Own evaluation and experimentation. Define offline metrics (e.g., NDCG, ERR, calibration) and online methodologies (A/B tests, interleaving, counterfactual & bandit approaches) to confidently attribute impact and guard against regressions.Champion safety & trust. Partner with policy and platform teams to encode safety standards and editorial principles into the ML system; create redteaming, adversarial, and safeguard layers for generative and curated experiences.Scale E2E ML systems. Collaborate with engineering on data contracts, feature stores, distributed training/inference, and automated rollout/rollback; drive architectural investments that increase agility and reliability of Discover's AI platform.Mentor & influence. Provide technical leadership across problem framing, methodology selection, code quality, and publishing/knowledgesharing; uplevel peers through design reviews, deepdives, and principled decisionStay close to users. Translate user engagements and behavioral history into model objectives and product bets; ensure our AI solutions elevate relevance, transparency, and engagement for real users. | |