Applied Sciencist - Frontier Tuning

Job d:

Location:

United States, Washington, Redmond Salary: USD $102,100 - $202,200 per year

Employment type:

Full-Time Work site: 3 days / week in-office

Role type:
Individual Contributor Travel:
Less than 25%
Profession:
Research, Applied, & Data Sciences Discipline:
Applied Sciences Company:
Microsoft Overview 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  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 Applied Scientists with solif 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.  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, cont rollability, or alignment approaches. Debugging, evaluation, or development tooling for foundation models. Multimodal model training, including language, vision, or diffusion models.

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.

Responsibilities Implement methods to adapt foundation models (e.g., language, diffusion, or multimodal models) for enterprise‑specific tasks such as document understanding, workflow automation, or content generation. Contribute to one or more aspects of the post-training stack, including:
Reinforcement learning or fine-tuning methods Architectural or parameter‑efficient adaptation techniques Inference‑time steering or cont rollability approaches Tooling for evaluation, debugging, or model development Harnesses Implement and evaluate adaptation approaches under real‑world enterprise deployment constraints such as latency, safety, privacy, policy compliance, and compute efficiency. Partner with research and engineering teams to translate product or customer requirements into scalable model adaptation solutions.

Explore post‑training techniques that improve domain specialization, tool use, planning, or agentic behaviors in enterprise environments. Drive technical work from concept to prototype, delivering new methods, systems components, or empirical insights that advance enterprise model customization. Document approaches and share best practices to improve organizational capabilities in post‑training and secure deployment of foundation models. Support mentorship and onboarding of interns or early‑career team members as appropriate.

Qualifications

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,…