Principal Applied AI/ML Research Scientist

Microsoft•Redmond, WA

About The Position

Overview Security represents the most critical priorities for our customers in a world awash in digital threats, regulatory scrutiny, and estate complexity. Microsoft Security aspires to make the world a safer place for all. We want to reshape security and empower every user, customer, and developer with a security cloud that protects them with end to end, simplified solutions. The Microsoft Security organization accelerates Microsoft’s mission and bold ambitions to ensure that our company and industry is securing digital technology platforms, devices, and clouds in our customers’ heterogeneous environments, as well as ensuring the security of our own internal estate. Our culture is centered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams and leaders to bring their best each day. In doing so, we create life-changing innovations that impact billions of lives around the world. The Microsoft Trust Data Privacy team is looking for an Applied AI/ML Research Scientist who thrives at the intersection of research rigor and engineering excellence. In this role, you will focus on developing and deploying state-of-the-art machine learning and AI models for large-scale data classification challenges. You will design, build, and ship end to end AI and machine learning systems—from problem framing and hypothesis testing, to scalable production deployment and ongoing monitoring. You’ll partner closely with product, engineering, and data teams to deliver measurable impact to customers. Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.

Requirements

Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Master's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 7+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelor's Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 10+ years data science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
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 background and Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

GenAI experience: fine‑tuning, instruction tuning, RAG pipelines, evaluation harnesses (e.g., task-specific metrics, human-in-the-loop).
Strong software engineering: Python + one systems language (C++/Java/Go/Rust), data structures/algorithms, code reviews, testing.
MLOps expertise: CI/CD (GitHub Actions/Azure DevOps), containers (Docker), orchestration (Kubernetes), model registry/feature store, monitoring & drift detection.
Experimentation: A/B testing design, statistical rigor; metrics for model quality and business impact (precision/recall, ROC/AUC, NDCG/MAP, uplift).
Data engineering: SQL, Spark/Databricks, data modeling, data quality and reproducibility.
Communication & execution: clear writing, design docs, stakeholder alignment, and consistent delivery to milestones.
Proven track record of shipping ML systems to production at scale (not just prototypes); portfolio or references welcome.
Applied research skills: reading SOTA literature, rapid replication, hypothesis-driven iteration, and practical adaptation to product constraints.

Responsibilities

Own end-to-end delivery: Lead the full modeling lifecycle for security scenarios, from data ingestion and curation to training, evaluation, deployment, and monitoring. Problem framing, literature review, model design, offline evaluation, online experimentation, and production deployment.
Implement and optimize models: Design and implement privacy-preserving data workflows, including anonymization, templating, synthetic augmentation, and quantitative utility measurement. Develop and maintain fine-tuning and adaptation recipes for transformer models, including parameter-efficient methods and reinforcement learning from human or synthetic feedback. Establish objective benchmarks, metrics, and automated gates for accuracy, robustness, safety, and performance, enabling repeatable model shipping.
Productionize AI & ML Collaborate with engineering and product teams to productionize models, harden pipelines, and meet service-level objectives for latency, throughput, and availability. Develop fine-tuning techniques for transformer models and establish benchmarks for accuracy, robustness, and performance to ensure reliable model delivery. Drive MLOps best practices: CI/CD, model registry, feature store, model serving, monitoring/drift.
Champion Responsible AI: fairness, explainability, privacy (GDPR/CCPA) and security considerations in model design and deployment. • Operational excellence: code quality, tests, observability (logs/metrics/traces), on-call ownership for ML services, and SLA adherence. • Collaborate cross‑functionally: write design docs/RFCs, partner with PMs and engineers, and drive execution towards predictable outcomes and timelines.

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