Principal Applied Scientist

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Copilot 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 AI‑powered 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 content‑quality stack, combining LLMs, multimodal models, and large‑scale recommender systems to drive measurable gains in engagement, satisfaction, and trust. You will set technical direction, mentor a high‑caliber science cohort, and partner closely with engineering, PM, UXR, and policy to ship end‑to‑end 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.

Required Qualifications:

• Bachelor'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)
  • 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.
• 2+ years of experience working with recommender systems/ranking or content‑quality/safety models at consumer scale, with clear business impact.
• 2+ years of experience in Python and at least one major deep learning framework (PyTorch/TensorFlow) with large‑scale data processing and training/inference on distributed systems.
• 2+ years of evaluation & experimentation (offline metrics, A/B testing, bandits) and ML model development lifecycle.

Preferred Qualifications:

• Master's Degree in Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g. machine learning, deep learning or similar technologies)
  • OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 6+ years related experience (e.g., statistics, predictive analytics, research)
  • OR equivalent experience.
• Have publications at top AI/ML conferences (e.g., KDD, SIGIR, EMNLP, NIPS, ICML, ICLR, RecSys, ACL, CIKM, CVPR, ICCV, etc.).
• Expertise with LLMs (prompting, finetuning, RAG), multimodal modeling, and retrieval‑augmented recommendation; familiarity with counterfactual learning and multi‑objective optimization.
• Experience building content integrity/safety systems (e.g., misinformation, harmful content, low‑quality/duplicate detection) and quality‑aware ranking.
• Demonstrated ability to lead cross‑disciplinary efforts (PM, ENG, UXR, editorial/policy) from idea to shipped impact; mentoring scientists and setting technical vision.
• Familiarity with Microsoft stack (e.g., Azure ML, Kusto, Synapse, Azure AI Foundry).

Applied Sciences IC5 - The typical base pay range for this role across the U.S. is USD $139,900 - $274,800 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 $188,000 - $304,200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

Microsoft will accept applications for the role until November 17, 2025.

#MicrosoftAI #mai #recsys #copilot #hiring

• Lead content‑quality 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 model‑level innovations; build human‑in‑the‑loop and active‑learning pipelines that get better over time.
• Advance the recommendation & ranking stack. Architect and productionize large‑scale DNN/LLM‑enhanced 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 red‑teaming, 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/knowledge‑sharing; uplevel peers through design reviews, deep‑dives, and principled decision‑
• Stay 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.