Sr. Applied Scientist, Alexa Excellence AI Ops, Alexa Excellence AI Ops
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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 a Principal Applied Scientist with strong research and systems‑building skills who is 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.
Post‑training may include reinforcement learning, fine‑tuning, architectural modification, inference‑time control, evaluation‑driven adaptation, or privacy‑preserving training techniques applied under real‑world enterprise deployment constraints.
Ideally, candidates will have experience in one or more of the following areas:
Scalable training systems for RLHF/RLAIF or other post‑training pipelines.
Scalable inference systems for LLMs.
Transformer architecture design or efficient adaptation techniques (e.g., LoRA-style methods).
Inference‑time steering, controllability, or alignment approaches.
Privacy-preserving machine learning (e.g., differential privacy or secure training).
Debugging, evaluation, or development tooling for foundation models.
Multimodal model training, including language, vision, or diffusion models.
This position is based at the Redmond campus with 3 days per week work in the office and 2 days per week work from home. Domestic relocation assistance is available.
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.
Design and develop methods to adapt foundation models (e.g., language, diffusion, or multimodal models) for enterprise‑specific tasks such as document understanding, workflow automation, or content generation.
Contribute to one or more aspects of the post-training stack, including:
Scalability and efficiency of training and inference systems
Reinforcement learning or fine-tuning methods
Architectural or parameter‑efficient adaptation techniques
Inference‑time steering or controllability approaches
Tooling for evaluation, debugging, or model development
Privacy- or security‑preserving training techniques (e.g., differential privacy)
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.
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 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.
Support mentorship and onboarding of interns or early‑career team members as appropriate.
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:
Applied Sciences IC6 - The typical base pay range for this role across the U.S. is USD $165,600 - $296,400 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $220,800 - $331,200 per year.
Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay
This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.
Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.
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Microsoft builds Windows, Azure, Office and the Copilot family of AI assistants, and operates one of the largest AI training and inference fleets in the world. Microsoft Research and the AI platform teams work across foundation models, systems for large-scale training, and applied ML in every product line.
Founded in 1975 and headquartered in Redmond, Washington, the company is also OpenAI's principal compute partner and ships AI tooling for developers through GitHub, VS Code and Azure AI.
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