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 solid 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(Reinforcement Learning from Human Feedback)/RLAIF (Reinforcement Learning from AI Feedback) or other post‑training pipelines.
Transformer architecture design or efficient adaptation techniques (e.g., LoRA-style methods).
Inference‑time steering, controllability, 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
Design and develop 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 controllability 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 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)
- 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.
- 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.
- 3+ years experience creating publications (e.g., patents, libraries, peer-reviewed academic papers).
- Experience presenting at conferences or other events in the outside research/industry community as an invited speaker.
- 3+ years experience conducting research as part of a research program (in academic or industry settings).
- 1+ year(s) experience developing and deploying live production systems, as part of a product team.
- 1+ year(s) 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:
- Transformer or multimodal model architectures.
- Reinforcement learning or post-training methods.
- Distributed or large‑scale ML training systems.
- 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.
- 2+ years of experience building scalable ML systems or pipelines for training, adapting, or deploying AI models.
- 2+ years of experience with Python and machine learning frameworks (e.g., PyTorch or equivalent).
- 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.
- Debugging, evaluation, or development tooling for foundation models.
Applied Sciences IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $160,200 - $261,000 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This 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.
Skills Required
- Bachelor’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 4+ years of related experience
- Master’s degree in a relevant field and 3+ years of related experience
- Doctorate in a relevant field and 1+ year of related experience
- Equivalent experience may substitute for the stated education and experience requirements
- Ability to meet Microsoft, customer, and/or government security screening requirements
- Microsoft Cloud Background Check upon hire or transfer and every two years thereafter
- Master’s degree in a relevant field and 6+ years of related experience
- Doctorate in a relevant field and 3+ years of related experience
- 3+ years creating publications, patents, libraries, or peer-reviewed academic papers
- Experience presenting at conferences or other external research or industry events as an invited speaker
- 3+ years conducting research in academic or industry settings
- 1+ year developing and deploying live production systems as part of a product team
- 1+ year developing and deploying products or systems across multiple product-cycle stages, from ideation to shipping
- Experience with foundation model training or adaptation in research, open-source, or production environments
- Experience with Transformer or multimodal model architectures
- Experience with reinforcement learning or post-training methods
- Experience with distributed or large-scale machine learning training systems
- Experience evaluating or benchmarking AI systems
- Experience with tool use, planning, or agentic model behaviors
- Experience deploying AI solutions in enterprise or customer environments
- Experience publishing academic papers as a lead author or essential contributor, or contributing to relevant leading-conference technical work
- 2+ years building scalable machine learning systems or pipelines for training, adapting, or deploying AI models
- 2+ years of experience with Python and machine learning frameworks such as PyTorch
- Experience with reinforcement learning or supervised fine-tuning for foundation models
- Experience building scalable RLHF, RLAIF, or other post-training pipelines
- Experience with Transformer architecture design or parameter-efficient adaptation techniques such as LoRA
- Experience with inference-time steering, controllability, or alignment approaches
- Experience with debugging, evaluation, or development tooling for foundation models
Microsoft Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Microsoft and has not been reviewed or approved by Microsoft.
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Fair & Transparent Compensation — Pay is presented as broadly competitive overall, with clear role/level/location variation and an emphasis on using posted ranges and band information for apples-to-apples comparisons.
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Retirement Support — Retirement benefits are described as a standout, highlighted by a strong 401(k) match structure and immediate vesting, plus additional plan features for tax-advantaged saving.
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Parental & Family Support — Family-oriented benefits are portrayed as a meaningful strength, with substantial paid parental leave and added supports like back-up care and adoption/surrogacy assistance.
Microsoft Insights
What We Do
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