Principal Applied Scientist

Frontier Tuning is Microsofts AI customization platform that enables enterprises to adapt foundation models to their unique workflows domains and datawhile preserving security privacy and reliability at scale. As we grow Frontier Tuning is becoming a critical pillar for ensuring enterprisespecific capabilities are systematically learned and reflected across Microsoft 365 and beyond.

We are seeking a Principal Applied Scientist with strong research and systemsbuilding skills who is excited to push the frontier of largescale model posttraining 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 enterpriseready AI systems.

Posttraining may include reinforcement learning finetuning architectural modification inferencetime control evaluationdriven adaptation or privacypreserving training techniques applied under realworld enterprise deployment constraints.

Ideally candidates will have experience in one or more of the following areas:

• Scalable training systems for RLHF/RLAIF or other posttraining pipelines.

• Scalable inference systems for LLMs.

• Transformer architecture design or efficient adaptation techniques (e.g. LoRA-style methods).

• Inferencetime 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.

This position is based at the Redmond campus with 3 days per week work in the office and 2 days per week work from home. Domestic relocation assistance is available.

Microsofts 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.

• Design and develop methods to adapt foundation models (e.g. language diffusion or multimodal models) for enterprisespecific tasks such as document understanding workflow automation or content generation.

• Contribute to one or more aspects of the post-training stack including:

  • Scalability and efficiency of training and inference systems

  • Reinforcement learning or fine-tuning methods

  • Architectural or parameterefficient adaptation techniques

  • Inferencetime steering or controllability approaches

  • Tooling for evaluation debugging or model development

  • Privacy- or securitypreserving training techniques (e.g. differential privacy)

  • Harnesses

• Implement and evaluate adaptation approaches under realworld 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 posttraining 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 posttraining and secure deployment of foundation models.

• Support mentorship and onboarding of interns or earlycareer team members as appropriate.

Required Qualifications:

• Bachelors Degree in Statistics Econometrics Computer Science Electrical or Computer Engineering or related field AND 8 years related experience (e.g. statistics predictive analytics research)
  • OR Masters 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 5 years related experience (e.g. statistics predictive analytics research)
  • OR equivalent experience.

Other Requirements:

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:

• Masters Degree in Statistics Econometrics Computer Science Electrical or Computer Engineering or related field AND 12 years related experience (e.g. statistics predictive analytics research)
  • OR Doctorate in Statistics Econometrics Computer Science Electrical or Computer Engineering or related field AND 8 years related experience (e.g. statistics predictive analytics research)
  • OR equivalent experience.
• 3 years experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
• 7 years experience conducting research as part of a research program (in academic or industry settings).
• 5 years experience developing and deploying live production systems as part of a product team.
• 7 years experience developing and deploying products or systems at multiple points in the product cycle from ideation to shipping.
• Experience contributing to research open-source systems or production deployments involving foundation model training or adaptation.
• Experience in one or more of the following areas:
• Scalable training and inference infrastructure design and implementation
• Transformer or multimodal model architectures.
• Reinforcement learning or post-training methods.
• Distributed or largescale ML training systems.
• Privacy-preserving ML (e.g. differential privacy).
• Evaluation or benchmarking of AI systems.
• Tool use planning or agentic model behaviors.
• Deployment of AI solutions in enterprise or customer environments.
• Experience publishing academic papers as a lead author or essential contributor or contributing to technical work presented at leading conferences in relevant research domains.
• 4 years of experience building scalable ML systems or pipelines for training adapting or deploying AI models.
• 4 years of experience with Python and machine learning frameworks (e.g. PyTorch or equivalent).

Applied Sciences IC6 - The typical base pay range for this role across the U.S. is USD $165600 - $296400 per year. There is a different range applicable to specific work locations within the San Francisco Bay area and New York City metropolitan area and the base pay range for this role in those locations is USD $220800 - $331200 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
position will be open for a minimum of 5 days with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age ancestry citizenship color family or medical care leave gender identity or expression genetic information immigration status marital status medical condition national origin physical or mental disability political affiliation protected veteran or military status race ethnicity religion sex (including pregnancy) sexual orientation or any other characteristic protected by applicable local laws regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process read more about requesting accommodations.

Required Experience:

IC