Principal Applied Scientist at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

03 Mar, 26

Salary

0.0

Posted On

03 Dec, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Deep Learning, Machine Learning, Statistics, Predictive Analytics, Recommender Systems, Python, TensorFlow, PyTorch, Evaluation, Experimentation, Multimodal Modeling, AI Generative Content, Sequence Modeling, Retrieval, Ranking, Slate Optimization

Industry

Software Development

Description

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. Advance user and content understanding. Drive advancement on user and content understanding to produce high quality and rich signals to the recommenders, across signal acquisition, understanding and utilization. Innovate on AI-forward features. Innovate and drive product transformation to a modern experience with AI-forward and companion-like features, such as AI Generative content, GenUI experiences. 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. 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) 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. Demonstrated ability to lead cross‑disciplinary efforts (PM, ENG, UXR, editorial/policy) from idea to shipped impact; mentoring scientists and setting technical vision. 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.

Responsibilities

Advance the recommendation and ranking stack by architecting and productionizing large-scale DNN/LLM-enhanced recommenders. Drive product transformation with AI-forward features and ensure high-quality user and content understanding.