Principal Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

17 Feb, 26

Salary

0.0

Posted On

19 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Deep Learning, Recommender Systems, Python, TensorFlow, PyTorch, Statistics, Predictive Analytics, Content Quality, Safety Models, Evaluation, Experimentation, Multimodal Modeling, Adversarial Techniques, Data Processing, Model Development

Industry

Software Development

Description

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. 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) 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. 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.

Responsibilities

Lead content-quality understanding at scale and design models to assess various content attributes. Collaborate with engineering to scale end-to-end ML systems and mentor peers in technical leadership.