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Research Machine Learning Federated Learning Jobs in Frederick, MD

Varsity Tutors

Machine Learning Tutor

Leesburg, VA · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Varsity Tutors

Machine Learning Tutor

Rockville, MD · Remote

$18 - $40/hr

About the Job The Varsity Tutors Live Learning Platform has thousands of students looking for online Machine Learning tutors nationally. As a tutor on the Varsity Tutors Platform, you'll have the ...

Bespoke Labs

Machine Learning Engineer

Frederick, MD · On-site

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

Bespoke Labs

Machine Learning Engineer

Hagerstown, MD · On-site

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

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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 Aug 17, 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 job categories do people searching Research Machine Learning Federated Learning jobs in Frederick, MD look for?

The top searched job categories for Research Machine Learning Federated Learning jobs in Frederick, MD 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 Postdoc Associate in Quantum Networks Entanglement Layer Research

Southeastern Universities Research Association

Gaithersburg, MD • On-site

$80K - $90K/yr

Full-time

Re-posted 25 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.
Research Title: Quantum Networks Entanglement Layer Research
The work will entail: 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.
Key responsibilities will include but are not limited to:
  • Characterize and develop models and optimizers for quantum network components.
  • Design optimization and learning algorithms and protocols for entanglement routing in quantum networks.
  • Prototype models, algorithms and experiments in simulated environments, and provide rigorous analyses and evaluations using measurement data from the project experimentation team.
  • Maintain a detailed log of all prototypes, simulations and experimental activities.
  • Share research findings through peer-reviewed journals, conferences, and presentations.

Qualifications
  • A PhD in Electrical and Computer Engineering or a related field is required.
  • Candidates should have research experience in one or more of the following areas: control and communication systems, convex/non-convex optimization, distributed and federated learning, dynamical networked systems; and with at least six (6) years of experience in theoretical and practical design and evaluation of machine learning and optimization algorithms for distributed systems and optimal network resource allocation.
  • A solid understanding of quantum network engineering, control and queueing theories, mathematics is essential, with at least three (3) years of experience working on mechanisms for supporting end-to-end quality of services in an emerging networking technologies.
  • Programming skills for control and communication systems and machine learning are required, with proficiency in languages such as MATLAB and Python, and with at least six (6) years of experience in independent design, efficient implementation, and testing of all aspects of optimization or machine learning algorithms in a testbed.
  • Familiarity with quantum network components and simulators is expected.
  • U.S. Citizen is preferred.

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.
PREP0004212

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