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Research Machine Learning Federated Learning Jobs in Frederick, MD

Axle

Director of Data Solutions

Rockville, MD · On-site

Axle is a bioscience and information technology company specializing in translational research ... federated learning/inference) and/or sensitive data platforms. • Experience leading simulation ...

Axle

Director of Data Solutions

Rockville, MD · On-site

... research, biomedical informatics, and data science applications. The Director of Data Solutions is ... federated learning/inference) and/or sensitive data platforms. • Experience leading simulation ...

AKIVA AI, LLC

AI Research Scientist

Leesburg, VA · On-site

$150 - $200/hr

PhD in Computer Science, Machine Learning, Statistics, or related discipline (or equivalent research output) * Strong implementation skills in PyTorch and modern ML tooling * Track record of ...

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All JobsResearch Machine Learning Federated Learning JobsResearch Machine Learning Federated Learning Jobs in Frederick, MD

Research Machine Learning Federated Learning information

See Frederick, MD salary details

$25.4K

$42.3K

$87.5K

How much do research machine learning federated learning jobs pay per year?

As of Sep 7, 2026, the average yearly pay for research machine learning federated learning in Frederick, MD is $42,340.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,300.00 and $45,700.00 per year, depending on experience, location, and employer.

What is a researcher in machine learning federated learning?

A Researcher in Machine Learning Federated Learning is a professional who investigates and develops methods to train machine learning models across multiple decentralized devices or servers, while keeping data localized and private. Their work focuses on improving algorithms, ensuring data privacy, and addressing challenges related to distributed learning, communication efficiency, and model accuracy. They often collaborate with other researchers, publish findings, and contribute to advancing technologies that make it possible to use sensitive data for AI without compromising privacy.

What are the key skills and qualifications needed to thrive as a researcher in machine learning federated learning?

To thrive as a Researcher in Machine Learning Federated Learning, you need a strong background in computer science, mathematics, and machine learning, typically supported by a relevant advanced degree (e.g., PhD or MSc). Familiarity with Python, TensorFlow, PyTorch, and distributed computing frameworks, as well as knowledge of privacy-preserving techniques and relevant research publications, is essential. Excellent analytical thinking, problem-solving abilities, and clear scientific communication are key soft skills for success in collaborative research environments. These competencies are vital to drive innovation, rigorously evaluate federated learning approaches, and advance privacy-preserving AI technologies.

What are some common challenges faced when implementing federated learning in a research environment?

One of the primary challenges in research-focused federated learning roles is ensuring data privacy and security while maintaining model performance across distributed devices. Researchers must also address issues such as handling heterogeneous data sources, communication bottlenecks between nodes, and the complexity of debugging decentralized systems. Collaborating with cross-functional teams—such as data engineers, privacy experts, and domain specialists—is vital to overcome these hurdles and drive successful outcomes. Staying updated with the latest advancements and actively contributing to open-source initiatives can also help researchers address these evolving challenges.

What is the difference between Research Machine Learning Federated Learning vs Data Scientist?

AspectResearch Machine Learning Federated LearningData Scientist
CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, academic institutions, tech companies focusing on privacy-preserving MLBusiness environments, analytics teams, data-driven departments
Industry UsageDeveloping federated algorithms, privacy-preserving ML modelsData analysis, modeling, reporting, and insights generation

Research Machine Learning Federated Learning specialists focus on developing privacy-preserving algorithms across distributed data sources, often in research or R&D settings. Data Scientists analyze and interpret data to inform business decisions. While both roles require strong ML knowledge, federated learning roles emphasize distributed systems and privacy, whereas Data Scientists focus on data analysis and visualization.

What are popular job titles related to Research Machine Learning Federated Learning jobs in Frederick, MD?

For Research Machine Learning Federated Learning jobs in Frederick, MD, the most frequently searched job titles are:

What cities near Frederick, MD are hiring for Research Machine Learning Federated Learning jobs?

Cities near Frederick, MD with the most Research Machine Learning Federated Learning job openings:

Research Machine Learning Federated Learning jobs near you

NIST PREP Research Associate in Performance of Machine Learning and AI Implementation in Economics a

Southeastern Universities Research Association

Gaithersburg, MD • On-site

$84K/yr

Full-time

Re-posted 14 days ago

Job description

This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas. Employees in this position will perform technical work that underpins the scientific research of the collaboration.
The position is in the Applied Economics Office (AEO), a part of the Engineering Laboratory (EL) at NIST, which provides economic products and services through research and consulting to industry and government agencies in support of productivity enhancement, economic growth, and international competitiveness, with a focus on improving the life-cycle quality and economy of constructed facilities and manufacturing processes that support social and economic functions. AEO is integrated within EL's major research thrusts. AEO delivers high quality research and tool development that informs and assists stakeholders in their decision-making processes. The position will collaborate directly with the software development team in EL's ELDST (Engineering Laboratory Data, Security, & Technology) that oversees AEO's software development projects.
Research Title: Performance and efficacy of machine learning and artificial intelligence implementation in economics and life cycle system science decision support
The work will entail: We are looking for a computer scientist (US citizen) to join our team in researching the performance and efficacy of machine learning and artificial intelligence (large language models - LLMs) across a variety of both research-oriented and public-facing software applications using standardized science-based metrics. The initial focus is on developing LLM-based outputs, and comparing their performance relative to manually developed outputs, including drafting annotated bibliographies and literature reviews, writing code, web applications that incorporate LLMs to enhance capabilities, and LLM-based web applications.
The ideal candidate will have a strong background in integrating LLM APIs, React, and front-end programming, and will be responsible for transforming models into usable APIs or integrated tools for production. They will also be responsible for monitoring, troubleshooting and enhancing model efficiency and scalability. The candidate should also be well versed in handling data preprocessing, and analysis for model training. The candidate should be aware of various prompt engineering techniques, implementing intelligent prompt caching (e.g., Redis), understanding of vector stores (e.g., Pinecone), and efficient token management. The candidate should have knowledge of implementing AI security protocols, including guardrails and techniques to prevent prompt injection. The candidate should also have a working knowledge of RAG (Retrieval Augmented Generation). Additional research tasks may be assigned based on candidate's skillset and priorities.
Key responsibilities will include but are not limited to:
  • Develop user interfaces for web application
  • Assist with special software development projects as assigned
  • Write and implement efficient code
  • Document code and publish on GitHub
  • Work closely with other developers
  • Statistically compare performance across code and tool designs
  • Draft manuscripts documenting the methodology and results

Qualifications
  • US Citizen
  • Master's degree in Computer Science or related field
  • At least 2 years of professional experience
  • At least 1 year as development team lead for at least one web application using the software stacks listed below
  • Experience with state management in React (RxJS)
  • Experience working on cloud technologies (AWS, Azure)
  • Working knowledge of RAG (Retrieval Augmented Generation)
  • Proficient with integrating one or more LLMs into applications (e.g., OpenAI, Gemini, Llama)
  • Proficient with HTML, CSS, Typescript, React, and Python
  • Proficient using any UI Component libraries (e.g., Ant Design, Material UI, etc.)
  • Proficient with Node.js
  • Working knowledge of building quick prototypes using Streamlit (or similar) and LLMs
  • Proficient with JSON
  • Proficient with Vite, Nginx, GitHub, Docker, and Portainer
  • GPU programming or data visualization experience a plus
  • Evidence of strong oral and written communication skills, including authorship on at least 2 technical publications)
  • Strong logical thinking and problem solving
  • Excellent attention to detail

Privacy Act StatementAuthority: 15 U.S.C. § 278g-1(e)(1) and (e)(3) and 15 U.S.C. § 272(b) and (c)
Purpose: The National Institute for Standards and Technology (NIST) hosts the Professional Research Experience Program (PREP) which is designed to provide valuable laboratory experience and financial assistance to undergraduates, post-bachelor's degree holders, graduate students, master's degree holders, postdocs, and faculty.
PREP is a 5-year cooperative agreement between NIST laboratories and participating PREP Universities to establish a collaborative research relationship between NIST and U.S. institutions of higher education in the following disciplines including (but may not be limited to) biochemistry, biological sciences, chemistry, computer science, engineering, electronics, materials science, mathematics, nanoscale science, neutron science, physical science, physics, and statistics. This collection of information is needed to facilitate administrative functions of the PREP Program.
Routine Uses: NIST will use the information collected to perform the requisite reviews of the applications to determine eligibility, and to meet programmatic requirements. Disclosure of this information is also subject to all the published routine uses as identified in the Privacy Act System of Records Notices: NIST-1: NIST Associates.
Disclosure: Furnishing this information is voluntary. When you submit the form, you are indicating your voluntary consent for NIST to use of the information you submit for the purpose stated.
SURA is an Equal Opportunity Employer. We believe that no one should be discriminated against because of their differences, such as age, disability, ethnicity, gender, gender identity and expression, religion, or sexual orientation. All employment decisions shall be made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status, or any other basis as protected by federal, state, or local law.
PREP0004070

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