Principal Applied Scientist
Microsoft · Posted Oct 12
Software, cloud computing, artificial intelligence, and hardware services
Get a personal compatibility score
Add a resume for personal matches
About the role
Microsoft Cloud Operations and Innovation (CO+I) underpins Microsoft's global cloud infrastructure, driving the innovation, planning, design, construction, and operation of one of the largest data center fleets in the world. The Data Center Applied AI team integrates cutting-edge, multimodal agentic AI systems into the core tools and operational workflows that power Microsoft's data centers. This Principal Applied Scientist role bridges state-of-the-art AI research with production-grade engineering, delivering reliable, secure, scalable and observable agent architectures spanning workflow automation, information retrieval, retrieval-augmented generation, tool and function calling, long-horizon task execution, and multi-agent orchestration.
What you will do
- Apply deep expertise in Generative AI, deep learning, NLP, and multimodal models to translate cutting-edge research into high-impact, production-ready AI solutions.
- Design and execute experiments that measurably improve agent planning, memory, grounding, reasoning, and long-horizon task completion.
- Perform lightweight fine-tuning (e.g., LoRA and related techniques) on multimodal and large multimodal language models—to improve entity recognition, reasoning accuracy, and response quality.
- Implement state-of-the-art approaches using foundation models, advanced prompt engineering, RAG, knowledge graphs, and multi-agent architectures, complemented by classical ML techniques where appropriate.
- Architect and implement end-to-end agent workflows that decompose user intent into executable plans, intelligently select and invoke tools, and recover gracefully from errors and edge cases.
- Rapidly prototype solutions and partner with engineering teams to drive production deployment, including debugging live systems and building AIOps workflows.
- Use data and telemetry to identify AI quality gaps, generate insights, and deliver proofs of concept that apply research innovations to real-world operational challenges.
- Design robust multi-step reasoning and tool-use strategies, including function calling, code execution, APIs, and secure connectors, with strong safety and reliability guardrails.
- Drive production excellence across latency, reliability, cost efficiency, observability, monitoring, and safe fallback behaviors.
- Define task-level evaluation metrics for agentic behavior, including success rate, tool-call accuracy, step efficiency, hallucination rate, safety violations, time-to-completion, and user satisfaction.
- Build and maintain offline and online evaluation pipelines, including:
- Golden datasets, scenario simulators, and regression test suites
- Human-in-the-loop evaluation frameworks and rubric design
- A/B experimentation and telemetry-driven iteration loops
- Partner with business and engineering stakeholders to translate requirements into clear technical specifications, research plans, and delivery milestones.
- Lead design reviews and influence engineering decisions across agent frameworks, model integration, and system architecture.
- Mentor and support other applied scientists and engineers, raising the bar for applied AI execution and production quality
Skills used in this role
What the employer is looking for
- PhD in Computer Science, Machine Learning, AI, NLP, or related field, OR MS with significant experience delivering production-grade AI systems.
- 10+ years of hands-on experience applying NLP, and/or LLMs to real-world production problems.
- Strong foundation in Large Language Models (LLMs), Generative AI, deep learning, and modern NLP.
- Proven experience building production-grade LLM systems, including prompt engineering, RAG, and multi-step reasoning pipelines.
- Solid expertise in chunking strategies and embedding selection, including tradeoffs in retrieval quality, context size, and downstream task performance.
- Experience designing and operating vector databases, including indexing strategies, similarity metrics, and retrieval optimization.
- Experience supporting production operations (AIOps/MLOps), including monitoring, logging, quality metrics, and debugging live systems.
- Proficiency in Python and experience with modern ML frameworks (e.g., PyTorch, TensorFlow) and model serving/inference.
- Strong applied experimentation skills: defining metrics, analyzing results, and iterating based on data.
- Ability to collaborate effectively with engineering, product, and business stakeholders.
- 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
- Experience with Cloud technology Stack
- Experience with parameter-efficient fine-tuning techniques (e.g., LoRA) for LLMs or multimodal models.
- Hands-on experience building agentic or tool-augmented LLM systems, including function calling, planners, and API/tool integration.
- Experience with advanced RAG architectures, such as hybrid retrieval, reranking, grounding, and citation strategies.
- Strong AIOps depth, including quality drift detection, alerting, rollback, and telemetry-driven optimization.
- Experience optimizing systems for latency, reliability, scalability, and cost efficiency at enterprise scale.
- Prior experience working on mission-critical or large-scale enterprise AI systems.
- Demonstrated mentorship or technical leadership within applied science or engineering teams.
Benefits and support
- Compensation and benefits are detailed in the job posting
About Microsoft
Microsoft Corporation is a multinational technology company that develops, licenses, and supports a wide range of software products, services, and hardware devices. Best known for its Windows operating systems, Microsoft 365 productivity suite, and Xbox gaming consoles, the company has expanded heavily into enterprise cloud computing via Azure and advanced artificial intelligence solutions. As one of the world's most valuable public enterprises, it empowers individuals and businesses globally through innovative digital transformation.
- Industry
- Software
- Company size
- 228,000+ employees
- Founded
- April 4, 1975
- Location
- Redmond, Washington, USA
- Funding stage
- Public Company
Leadership
Chairman and Chief Executive Officer
Chief Financial Officer
Vice Chair and President
Recent coverage
Windows Central
Microsoft Announces Windows 11 Version 26H2 and Previews Next-Gen AI Features2026-10-04
Barchart
Wells Fargo Adds Microsoft to Q4 Tactical Ideas List With Street-High Target2026-10-06
The Official Microsoft Blog
Microsoft 365 and LinkedIn Leadership Update Issued by Satya Nadella2026-10-01