Applied Scientist

Applied Scientist

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 Applied Scientists with strong systems-building skills who are 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.

We welcome candidates with experience in one or more of the following areas:

• Scalable training systems for RLHF/RLAIF or other post-training pipelines.
• Transformer architecture design or efficient adaptation techniques (e.g., LoRA-style methods).
• Inference-time steering, controllability, or alignment approaches.
• Debugging, evaluation, or development tooling for foundation models.
• Operating and maintaining AI production systems.

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:

• 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

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.
• 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.

Qualifications:

Required Qualifications:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ 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 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field
• 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:

• Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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 3+ years related experience (e.g., statistics, predictive analytics, research)
• OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
• OR equivalent experience.
• 1+ year(s) experience creating publications (e.g., patents, peer-reviewed academic papers).
• Experience contributing to open-source systems, or production deployments involving foundation model training or adaptation.
• Experience in one or more of the following areas:
• Reinforcement learning or post-training methods
• Distributed or large-scale ML training systems
• Evaluation or benchmarking of AI systems
• Tool use, planning, or agentic model behaviors
• Deployment of AI solutions in enterprise or customer environments
• 1+ 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 (e.g., PyTorch or equivalent)