Required Qualifications:
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
- OR equivalent experience.
- Experience developing and deploying large language models (LLMs), including agentic systems, supervised fine-tuning, and Reinforcement Learning (RLHF)
- Experience designing, implementing, and optimizing Retrieval-Augmented Generation (RAG) pipelines and advanced context engineering.
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings:
- Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
Preferred Qualifications:
- Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical OR Computer Engineering, OR related field AND 5+ 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 3+ years related experience (e.g., statistics, predictive analytics, research)
- OR Doctorate in Statistics, Econometrics, Computer Science, Electrical OR Computer Engineering.
- 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
- Hands-on experience with modern LLM evaluation techniques, including LLM-as-a-Judge, agentic evaluations, and RAG assessments.
- Experience delivering successful, large-scale applied ML projects in an industry setting.
- Deep understanding of fundamental ML algorithms (supervised, unsupervised) and modern neural network architectures.
- Experience with MLOps practices, including model versioning, automated testing, monitoring, and CI/CD for machine learning.
- A record of publication in top-tier scientific venues (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, KDD).
- Ability to translate complex ML concepts into business value and communicate technical insights to non-technical stakeholders.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: