Senior Applied Scientist

Build high-fidelity synthetic and manufactured datasets, along with rigorous evaluation sets and benchmarks that mirror the workflows of enterprise information-workers. Build a clear, inspiring vision that aligns every team member around ambitious, measurable goals. Innovation with LLMs: Stay at the cutting edge of NLP (natural language processing) and large language models. Apply techniques such as prompt engineering, fine-tuning, and retrieval-augmented generation (RAG) to enhance Copilot's capabilities. You will explore new model architectures and external knowledge integration to push the boundaries of what Copilot can do in the calendar domain. Leverage large language models and diverse data (emails, meetings, documents, chat transcripts etc) to create intelligent solutions for time management. Modeling & Personalization: Architect and refine machine learning models (supervised and unsupervised) that optimize the relevance and personalization of calendar features. Experimentation & Evaluation: Continuously iterate on models using real user feedback and telemetry, ensuring each new version of the Copilot delivers higher precision, recall and better user satisfaction. Product Integration: Work closely with engineering and product teams to integrate your AI models into Calendar. Ensure solutions are production-ready - meeting standards for scalability, security, compliance, and real-time performance in a cloud environment. Technical Leadership & Mentorship: Provide technical leadership within the team and across partner groups. Mentor applied scientists and machine learning engineers, fostering best practices in research, experimentation, and coding. Guide technical initiatives and ensure scientific rigor in how the team builds and evaluates AI solutions. Champion a culture of collaboration, learning, and rapid innovation to continuously improve our AI-powered productivity features. Team Building: Set a bold, customer-centric mission that galvanizes the team and clarifies priorities. Assemble complementary skill sets, cultivate candid collaboration, celebrate innovation, clear roadblocks, amplify wins, and hold a high bar for accountability—turning collective momentum into outsized, repeatable results. Provide expertise in building and scaling relevance and ranking systems, including experience with retrieval, embeddings, and evaluation methodologies tailored to LLM-powered applications. Demonstrate leadership in developing AI/ML solutions for productivity or assistant-like experiences, with a strong track record of managing cross-functional collaborations and driving measurable impact through data-driven product iteration. Bachelor'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 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, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) These requirements include but are not limited to the following specialized security screenings: Master'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 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. 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers). 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. Experience with modern LLM evaluation techniques, including LLM-as-a-Judge, agentic evaluations, and RAG assessments. Experience with MLOps practices, including model versioning, automated testing, monitoring, and CI/CD for machine learning. Experience with a 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.