Senior Applied Scientist

Microsoft • Redmond, WA

7d

About The Position

Frontier Tuning is Microsoft’s AI customization platform, enabling enterprises to adapt foundation models to their workflows, domains, and data while preserving security, privacy, and reliability at scale. The Senior Applied Scientist will advance large-scale model post-training and adaptation, combining algorithmic research with the design and development of scalable infrastructure and tooling for training, steering, evaluating, and securely deploying enterprise-ready AI systems.

The work may involve reinforcement learning, fine-tuning, architectural modification, inference-time control, evaluation-driven adaptation, or privacy-preserving training under real-world enterprise deployment constraints. The role focuses on adapting foundation models—including language, diffusion, and multimodal models—to enterprise needs, and on translating research and product or customer requirements into scalable solutions. The scientist will drive technical work from concept to prototype, document and share approaches, and support interns or early-career colleagues as appropriate.

Microsoft describes its mission as empowering every person and organization to achieve more, and emphasizes growth mindset, innovation, collaboration, respect, integrity, accountability, and inclusion. This position requires passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter. The role is classified as Applied Sciences IC4. The position will be open for a minimum of five days, with applications accepted on an ongoing basis until filled.

Requirements

• Bachelor’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 4+ years of related experience (such as statistics, predictive analytics, or research); or a master’s degree in one of these fields and 3+ years of related experience; or a doctorate in one of these fields and 1+ year of related experience; or equivalent experience.
• Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.
• 2+ years of experience 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 or equivalent.

Nice To Haves

• Master’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 6+ years of related experience; or a doctorate in one of these fields and 3+ years of related experience; or equivalent experience.
• 3+ years of experience creating publications, such as patents, libraries, or peer-reviewed academic papers.
• Experience presenting at conferences or other events in the outside research or industry community as an invited speaker.
• 3+ years of experience conducting research as part of an academic or industry research program.
• 1+ year of experience developing and deploying live production systems as part of a product team.
• 1+ year of 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: transformer or multimodal model architectures; reinforcement learning or post-training methods; distributed or large-scale machine-learning training systems; evaluation or benchmarking of AI systems; tool use, planning, or agentic model behaviors; or 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.
• Experience with reinforcement learning or supervised fine-tuning for foundation models.
• Experience with scalable training systems for RLHF, RLAIF, or other post-training pipelines.
• Experience with transformer architecture design or efficient adaptation techniques, such as LoRA-style methods.
• Experience with inference-time steering, controllability, or alignment approaches.
• Experience with debugging, evaluation, or development tooling for foundation models.

Responsibilities

• Design and develop methods to adapt foundation models, such as language, diffusion, or multimodal models, for enterprise-specific tasks including document understanding, workflow automation, and content generation.
• Contribute to one or more parts of the post-training stack, including reinforcement learning or fine-tuning methods, architectural or parameter-efficient adaptation techniques, inference-time steering or controllability, and tooling or harnesses for evaluation, debugging, or model development.
• Implement and evaluate adaptation approaches under 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 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior