Applied Scientist

Microsoft • Redmond, WA

2d • $102,100 - $219,200

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. As the platform grows, it is becoming a key part of bringing enterprise-specific capabilities to Microsoft 365 and other products.

Microsoft is seeking Applied Scientists with strong systems-building skills to advance large-scale model post-training and adaptation. The role combines algorithmic innovation with the design and development of scalable infrastructure and tooling for training, steering, evaluating, and securely deploying enterprise-ready AI systems. Relevant areas include scalable RLHF/RLAIF and other post-training pipelines, transformer architecture and efficient adaptation, inference-time steering and alignment, foundation-model evaluation and development tools, and operating AI production systems.

The scientist will develop and assess adaptation approaches against real-world enterprise constraints, collaborate with research and engineering teams to translate product or customer requirements into scalable solutions, drive technical work from concept through prototype, and document approaches and best practices.

Requirements

• Bachelor’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 2+ years of related experience; or a master’s degree in one of these fields and 1+ year of related experience; or a doctorate in one of these fields; 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.

Nice To Haves

• Bachelor’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 5+ years of related experience; 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.
• 1+ year of experience creating publications, such as patents or peer-reviewed academic papers.
• Experience contributing to open-source systems or production deployments involving foundation-model training or adaptation.
• Experience in reinforcement learning or post-training methods.
• Experience in 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.
• 1+ year 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.

Responsibilities

• Work on efficient hosting of large language models and parameter-efficient adapters.
• Develop architectural or parameter-efficient model adaptation techniques.
• Develop inference-time steering or controllability approaches.
• Build tooling for evaluation, debugging, or model development, including harnesses.
• 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.
• 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.

Benefits

• Certain roles may be eligible for benefits and other compensation; additional benefits and pay information is available through the provided Microsoft careers link.

What This Job Offers

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