Overview
We're hiring an Applied AI Engineer to join a fast-moving, high-ownership team building next-generation AI assistant and productivity capabilities. This role blends LLM product engineering, evaluation science, hillclimbing, and internal tool building with the pace and creativity of a startup. You'll work across the entire lifecycle of features from early prototypes to production-grade systems and help define how millions of users interact with AI.
Responsibilities
Design and ship LLM-powered assistant features, including conversational flows, agentic behaviors, retrieval pipelines, and multimodal interactions. Build prompt architectures, system instructions, and orchestration logic that ensure reliability, grounding, and personality consistency. Prototype new capabilities rapidly and iterate based on user signals and evaluation data.
Evaluation, Hillclimbing & Quality Systems
Build and maintain evaluation frameworks for correctness, safety, grounding, and UX quality. Run hillclimbing loops across prompts, models, and tool-use strategies to continuously improve assistant performance. Analyze failure modes, design mitigations, and drive systematic improvements across the stack.
LLM Tooling & Internal Infrastructure
Develop internal tools for prompt experimentation, model comparison telemetry and debugging automated eval pipelines. Create reusable frameworks that accelerate the entire AI org's ability to ship high-quality assistant features.
Applied ML & Product Integration
Integrate LLMs with product surfaces, APIs, and backend systems. Build lightweight ML components (ranking, classification, summarization, personalization) that enhance assistant intelligence. Collaborate with PM, design, and research to turn ambiguous ideas into polished user experiences.
High-Velocity Teamwork
Operate with startup-founder energy: bias for action, rapid iteration, and comfort with ambiguity. Work closely with researchers, engineers, and product leaders in a fast-moving AI team where ideas ship quickly and impact is immediate. Contribute to a culture of experimentation, clarity, and high-quality execution.
Qualifications
Required/minimum qualifications:
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field OR Masters Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelors Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 2+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience.
Preferred Qualifications
Doctorate in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 1+ year(s) data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Masters Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 3+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR Bachelors Degree in Data Science, Mathematics, Statistics, Econometrics, Economics, Operations Research, Computer Science, or related field AND 5+ years data-science experience (e.g., managing structured and unstructured data, applying statistical techniques and reporting results) OR equivalent experience. 2+ years shipping production-level code, models, or data analysis. 1+ years using AI-assisted coding and analysis techniques. Experience working on small teams and mid-stage startup environments. Experience working on AI products. PhD in engineering, applied math, statistics, or related analytical field. 4+ years shipping production-level code, models, or data analysis. Deep experience building from zero-to-one. Hands on work hillclimbing AI evaluations.
Compensation
Data Science IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 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 $131,400 - $215,400 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
Data Science IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 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 $158,400 - $258,000 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
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.