Applied Scientist
microsoftRedmond (WA)
About the role
Overview:
In this role you will advance enterprise-ready AI by developing scalable post-training and adaptation methods for foundation models. You will work with cross-functional teams to translate requirements into robust model customization systems. The work focuses on scalable infrastructure, evaluation tooling, and secure deployment to enterprise settings. This opportunity lets you shape practical AI capabilities that scale across Microsoft 365 and beyond. You will contribute to a culture that values innovation, collaboration, and impact.
Responsibilities:
Efficient hosting of large language models and parameter-efficient adapters Architectural or parameter-efficient adaptation techniques Inference-time steering or controllability approaches Tooling for evaluation, debugging, or model development Implementation and evaluation of adaptation approaches under real-world deployment constraints Collaborate with research and engineering to translate requirements into scalable solutions Advance methods from concept to prototype delivering new systems or insights Document approaches and share best practices for post-training and secure deployment
Key requirements:
Bachelor's degree in a related field and 2+ years of related experience OR Master's degree in a related field and 1+ year of related experience OR Doctorate in a related field or equivalent experience 2+ years experience with Python and machine learning frameworks (e.g., PyTorch) Experience with scalable ML training or post-training methods Experience in one or more of: RLHF/RLAIF, distributed ML systems, evaluation/benchmarking, deployment in enterprise environments Ability to meet security screening requirements Strong debugging, evaluation, and tooling development skills cross-functional collaboration problem solving effective communication Python PyTorch large-scale ML training systems
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.