Applied Scientist II

Microsoft

United States

On-site

USD 100,600 - 215,400

Full time

14 days+

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Benefits offered by this job

Health insurance

401(k) retirement plan

Performance bonuses

Job summary

A leading technology company is seeking an Applied Scientist II to focus on AI and ML, developing evaluation frameworks and analyzing data. Ideal candidates have significant experience in AI/ML model tuning and deep learning techniques. You'll work with cross-functional teams on groundbreaking projects within a dynamic research environment.

Qualifications

Bachelor's degree or higher in relevant areas.
2+ years related experience in statistics or analytics.
1+ years with AI/ML component productization.

Responsibilities

Design and implement evaluation frameworks.
Drive data synthesis and collection strategies.
Build pipelines to analyze algorithms and models.

Skills

Experience training/fine tuning AI/ML models

Deep learning techniques knowledge

Ability to analyze multimodal data

Education

Bachelor's Degree in Computer Science or related field

Tools

Python

PyTorch

TensorFlow

Job description

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Pay found in job post

Retrieved from the description.

Base pay range

$100,600.00/yr - $215,400.00/yr

The Microsoft Applied Sciences Group incubates disruptive technologies for Microsoft’s next-gen hardware products and is working on several exciting projects that will shape how computers and other devices perceive the user and the user’s environment. Operating as a startup within the company, this team works closely with several research and product teams to bring compelling new experiences to the market. A lot of these experiences will be powered by large language models, natural language processing (NLP), speech, and computer vision – and as part of this team, you will have the unique opportunity to develop and implement the ML algorithms and deep neural network (DNN) models that make magic happen.

We are looking for an Applied Scientist II – Applied Science in the field of Large Language Models (LLMs) and Small Language Models (SLMs). This role emphasizes evaluation and data synthesis as the foundation for building future intelligent and agentic systems.

You will design and operationalize robust evaluation metrics for multimodal embeddings, retrieval-augmented generation (RAG) pipelines, and agentic systems that combine reasoning, planning, and action. These metrics will not only measure performance but also guide the creation of new datasets, inform data synthesis and augmentation strategies, and identify opportunities for novel end-to-end solutions.

Through this evaluation-driven approach, you will shape the direction of agentic architectures, new model designs, and applied research initiatives, ensuring that our systems continuously adapt, improve, and expand their capabilities. The ability to analyze multimodal data and interpret human and human-object interactions is central to Applied Science’s mission of enabling seamless human-computer interaction.

As part of this team, you will collaborate with a growing group of talented researchers already dedicated to this mission and leverage data and hardware resources available to only a select few. Naturally, the opportunity for you to push the state of the art in this field is huge.

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

Design and implement evaluation frameworks to measure the performance of multimodal embeddings, RAG pipelines, and agentic systems.
Use evaluation insights to drive data synthesis, augmentation, and collection strategies that improve coverage and robustness.
Build pipelines to test algorithms and models, analyze the results, and translate findings into actionable improvements.
Develop metrics and benchmarks that inform system-level performance and guide the evolution of end-to-end human-computer interaction solutions.
Collaborate with researchers and engineers to prototype and scale agentic systems that combine reasoning, planning, and action.
Research and develop LLMs and SLMs with Python and other relevant programming languages.

Qualifications

Required Qualifications:

Bachelor's Degree in 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 Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in Computer Science, Electrical or Computer Engineering, or related field
OR equivalent experience.
1+ years of experience training/fine tuning AI/ML models, preferably LLMs/SLMs (small language model).
1+ years' experience with productization or shipping ML and/or AI components for large-scale internet applications.
Demonstrated experience with deep learning techniques (e.g., Transformers, RNNs, CNNs, reinforcement learning) and machine learning frameworks (Python, PyTorch, TensorFlow, ONNX).
Experience working with multimodal data and models (e.g., text, vision, speech, structured signals) and/or retrieval-augmented generation (RAG).

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 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 Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research)
OR Doctorate in 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 creating or deploying evaluation frameworks that drive model improvement, data synthesis, or system-level design.
Solid background in applied research with ability to translate evaluation insights into new models, end-to-end solutions, and research directions.
Proficiency with C/C++ or other performant programming languages a plus.

Applied Sciences 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

Microsoft will accept applications for the role until September 18, 2025.

Microsoft is an equal opportunity employer. Consistent with applicable law, 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 and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

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