About the job
The Core Recommendation Ranking team in Microsoft AI Copilot Discover Engineering Org is looking for a passionate and experienced applied scientist architect who wants to build the next generation of recommendations using advanced AI technologies, especially large language models, at scale. We are responsible for content ranking and reranking to deliver most engaging and high quality recommendation results. Our content include news feeds, interest feeds, video feeds, AIGC feeds, etc. We are seeking a Principal Applied Scientist Architect to integrate GenAI and agentic systems into end-to-end ranking stack. This role is ideal for a senior technical leader who combines deep expertise in large-scale recommendation systems, large language models and agentic systems, with the architectural vision to drive cross-team alignment, accelerate innovation, and deliver measurable impact across Microsoft surfaces. You will partner closely with engineering, product, and applied science teams to design, optimize, and scale intelligent ranking systems that power personalized content experiences for millions of users.
Responsibilities
Design & implement ranking, reranking, and retrieval models using deep learning, LLMs, and advanced recommendation techniques.
Architect the next generation of ranking, reranking, and retrieval systems for large-scale content recommendation scenarios, for example generative recommendations, agentic feeds, etc.
Lead the design of robust, efficient, and extensible ML/DL models pipelines, including feature engineering, model training, evaluation, and online inference.
Establish technical standards and best practices for experimentation, model governance, and system reliability.
Drive innovation in model architectures (e.g., deep learning, LLM-enhanced ranking, multi-task learning, contextual bandits, reinforcement learning).
Partner with engineering, product, and platform teams to align roadmaps, integrate new capabilities, and ensure seamless end-to-end delivery.
Invest in others’ growth and mentor team members, fostering a culture of scientific rigor, innovation, and operational excellence.
Regularly communicate team progress internally and evangelize progress and opportunities to a wider audience including management and leadership.
Mentor junior scientists and engineers, fostering a culture of technical excellence and knowledge sharing.
Qualifications
Minimum
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.
Preferred
Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 9+ years related experience (e.g., statistics, predictive analytics, research) 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. 8+ years of experiences in applied science, deep learning, or related fields, with a solid track record of delivering production ML systems at scale. Expertise in recommendation systems, ranking models, search relevance, or personalization. Experience applying LLM techniques or Recommendation system. Proficiency in modern ML frameworks (e.g., PyTorch, TensorFlow), data processing systems, and cloud-scale infrastructure. Demonstrated ability to lead cross-functional initiatives and influence technical direction across multiple teams. Solid communication skills with the ability to articulate complex technical concepts to diverse audiences. Experience with LLM-based ranking, agentic AI, or generative AI applied to recommendation or personalization. Publications in top-tier ML/AI conferences (e.g., NeurIPS, ICML, KDD, WWW, RecSys). Solid architectural skills with experience designing large-scale ML systems, distributed pipelines, and high-throughput online services. Experience working through full product cycles from initial design to final product delivery. Experience developing and designing backgrounds in multi-tiered distributed services. Experience with data structures, algorithms, asynchronous programming, and data processing. Knowledge and experience in large scale data analytics, such as Spark. Experience working with heterogeneous signals (behavioral, contextual, semantic embeddings) and multi-objective optimization. Experience developing end to end ML/DL systems.