Senior Applied Scientist

Microsoft•Redmond, WA

1d•$119,800 - $261,000

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. This role focuses on advancing large-scale model post-training and adaptation through both algorithmic research and the design and development of scalable infrastructure and tooling for training, steering, evaluating, and securely deploying enterprise-ready AI systems. Post-training work may involve reinforcement learning, fine-tuning, architectural modification, inference-time control, evaluation-driven adaptation, or privacy-preserving training techniques, subject to real-world enterprise deployment constraints. The position supports adapting language, diffusion, and multimodal models for enterprise-specific needs, and works with research and engineering teams to translate product or customer requirements into scalable solutions. The role also contributes to organizational knowledge-sharing and may support mentorship and onboarding of interns or early-career team members.

Requirements

Bachelor’s degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field and 4+ 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.
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.
Experience with one or more of the following: reinforcement learning or supervised fine-tuning for foundation models; scalable RLHF/RLAIF or other post-training systems; transformer architecture or efficient adaptation techniques; inference-time steering, controllability, or alignment; or foundation-model debugging, evaluation, or development tooling.

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 with transformer or multimodal model architectures; reinforcement learning or post-training methods; distributed or large-scale ML training systems; AI evaluation or benchmarking; 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.
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 such as PyTorch or equivalent.

Responsibilities

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