About the role
Develop scientific approaches for evaluating and improving LLM reasoning, grounding, retrieval and agentic behaviors using offline benchmarking, online experimentation and large-scale telemetry analysis. Drive complex technical investigations, model evaluations and experimentation to improve quality, reliability, latency, cost efficiency and user experience. Define and execute rigorous evaluation methodologies, offline benchmarks, online experimentation frameworks and data-driven approaches for measuring model and product performance. Partner closely with engineering and product teams to translate research innovations into scalable, production-ready systems used by millions of customers. Deeply analyze model behavior, failure modes, grounding effectiveness, reasoning quality, tool use, retrieval performance and orchestration outcomes to identify opportunities for improvement. Develop and advance approaches for agentic workflows, multi-step reasoning, planning, retrieval-augmented generation (RAG), tool orchestration and reinforcement-learning-based optimization. Review technical designs, experimental results and implementation approaches to ensure high standards of scientific rigor, reproducibility and engineering excellence. Collaborate with researchers, applied scientists and engineers to solve challenging technical problems spanning LLMs, machine learning, deep learning, information retrieval and human-AI interaction. Stay current with advances in Large Language Models, reasoning systems, agent architectures, reinforcement learning and applied AI, incorporating relevant innovations into production systems. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND demonstrated professional 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 demonstrated professional related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND demonstrated professional related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. A Ph.D. in Computer Science, Math, Physics, Statistics, or related areas. Candidates with master's degree with industry experience or publication record in the areas of Information Retrieval, Machine Learning, Natural Language Processing are considered as well. Demonstrated professional working as an IC in Machine Learning, Large Language Models, Natural Language Processing, Information Retrieval, Recommendation Systems or related fields. Professional experience developing ML models for scaled production services, with a proven track record of successfully shipping applied research to production . Experience designing and implementing agentic AI models and systems.
