Senior Applied Scientist - Language Understanding and Grounding Data Qualit at Microsoft

Redmond, Washington, United States -

Full Time

Start Date

Immediate

Expiry Date

08 Apr, 26

Salary

234700.0

Posted On

08 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Large Language Models, Machine Learning, Python, C#, Statistical Analysis, Predictive Analytics, Model Development, Model Evaluation, Fine-tuning, Evaluation Pipelines, Cross-functional Collaboration, Prompt Engineering, Reinforcement Learning, Cloud Platforms, Distributed Systems, Data Quality

Industry

Software Development

Description

Overview We turn everyday, messy queries into precise, actionable signals that power M365 Copilot and Microsoft Search. Language understanding (LU) sits on the critical path—it can run on every query—classifying intent and slots and shaping the downstream search plan across providers in the substrate stack. Our work ensures that all tool calls are appropriately scoped and parameterized so Copilot starts with the right grounding data, improving relevance and limiting hallucinations. As Senior Applied Scientist - Language Understanding and Grounding Data Quality, what you’ll work on Large Language Model (LLM)/SLMpowered LU: intent & slotting with calibrated confidence, optimized for low latency and high reliability in Copilot experiences MultiQuery (MQ) expansion: embrace ambiguity by exploring multiple interpretations and smarter fallbacks to boost recall without sacrificing precision. Finetuning & eval tooling: Our team works to improve model quality specific to tool calling: Making sure the right tool is called in the right way at the right time to get the best grounding data possible. To do it, we post-train LLMs iterating fast with robust datasets, prompt evaluation, and shiproom discipline to raise quality week over week through leading edge fine-tuning Global reach: help bring LU to more languages and markets with compliant modeling practices and scalable i18n strategy. Deep partner collaboration: work side-by-side with Relevance and Reasoning on unified strategies that blend lexical + semantic retrieval. 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 Improve LLM capabilities influencing grounding data quality. Apply latest research to post-train LLMs. Build and evaluate metrics and reward models. Add new functionality and ensure high quality for customers using Copilot on their Business data. Design and implement scalable evaluation pipelines for LU and grounding quality Collaborate with cross-functional teams to define success metrics and drive continuous improvement in Copilot experiences Qualifications Required Qualifications: Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ 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. Experience in Python, C# or similar programming languages for model development, training, and evaluation Experience with large-scale machine learning systems, including training, fine-tuning, and deployment of LLMs or similar models 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 Hands-on experience with prompt engineering, reinforcement learning from human feedback (RLHF), or reward modeling Familiarity with distributed systems and cloud platforms (e.g., Azure) for large-scale ML workloads Applied Sciences 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 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.

Responsibilities

The role involves improving LLM capabilities and ensuring high-quality grounding data for Copilot. Responsibilities also include designing scalable evaluation pipelines and collaborating with cross-functional teams.