Job description

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 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 a Principal Applied Scientist with strong research and systems‑building skills who is 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. Ideally, candidates will have experience in one or more of the following areas: • Scalable training systems for RLHF/RLAIF or other post‑training pipelines. • Scalable inference systems for LLMs. • 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. 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. 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: • Scalability and efficiency of training and inference systems • 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 • Privacy- or security‑preserving training techniques (e.g., differential privacy) • 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 8+ 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 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: • Master's 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 large‑scale 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 $165,600 - $296,400 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 $220,800 - $331,200 per year. Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: careers.microsoft.com 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.

Requirements

Must-haves

Ideally, candidates will have experience in one or more of the following areas:
Scalable training systems for RLHF/RLAIF or other post‑training pipelines.
Scalable inference systems for LLMs.
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.
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:
Scalability and efficiency of training and inference systems
Reinforcement learning or fine-tuning methods

Nice-to-haves

OR equivalent experience.
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 large‑scale 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.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpython

Platform: eightfold
Effort: medium

Benefits

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