Senior Applied Scientist

Frontier Tuning is Microsoft’s AI customization platform that enables enterprises to adapt foundation models to their unique workflows, domains, and data—while preserving security, privacy, and reliability at scale. As we grow, Frontier Tuning is becoming a critical pillar for ensuring enterprise‑specific capabilities are systematically learned and reflected across Microsoft 365 and beyond. We are seeking Senior Applied Scientist with strong research and systems‑building skills who are excited to push the frontier of large‑scale model post‑training and adaptation. This role spans algorithmic innovation as well as the design and development of scalable infrastructure and tooling for training, steering, evaluating, and securely deploying enterprise‑ready AI systems. Post‑training may include reinforcement learning, fine‑tuning, architectural modification, inference‑time control, evaluation‑driven adaptation, or privacy‑preserving training techniques applied under real‑world enterprise deployment constraints. We welcome candidates with experience in one or more of the following areas: Reinforcement learning or supervised fine‑tuning for foundation models; Scalable training systems for RLHF/RLAIF or other post‑training pipelines; Transformer architecture design or efficient adaptation techniques (e.g., LoRA-style methods); Inference‑time steering, controllability, or alignment approaches; Privacy-preserving machine learning (e.g., differential privacy or secure training); Debugging, evaluation, or development tooling for foundation models; Multimodal model training, including language, vision, or diffusion models