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Research Machine Learning Federated Learning Jobs in Washington

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

Conduct research to identify new approaches and methods for machine learning and AI. * Stay updated with the latest trends and advancements in machine learning and AI. * Document processes, codes ...

SAIC

Machine Learning Developer

Arlington, VA · Hybrid

Conduct research to identify new approaches and methods for machine learning and AI. * Stay updated with the latest trends and advancements in machine learning and AI. * Document processes, codes ...

Full Scope

Machine Learning Engineer

Reston, VA

Stay current with the latest research and advancements in machine learning and AI. * Participate in code reviews, team meetings, and contribute to a collaborative development environment. * Document ...

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Research Machine Learning Federated Learning information

What are the key skills and qualifications needed to thrive as a Researcher in Machine Learning Federated Learning, and why are they important?

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 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 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 Washington? For Research Machine Learning Federated Learning jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Research Machine Learning Federated Learning jobs in Washington look for? The top searched job categories for Research Machine Learning Federated Learning jobs in Washington are:
What cities in Washington are hiring for Research Machine Learning Federated Learning jobs? Cities in Washington with the most Research Machine Learning Federated Learning job openings:
Research Machine Learning Federated Learning jobs near you
Machine Learning Developer

Machine Learning Developer

SAIC

Arlington, VA • Hybrid

Full-time

Posted 21 days ago

SAIC rating

7.8

Company rating: 7.8 out of 10

Based on 78 frontline employees who took The Breakroom Quiz

68th of 203 rated it services

Job description

Job ID: 2611091

Location: Arlington, VA, US

Date Posted: 2026-05-05

Category: Software

Subcategory: SW Engineer

Schedule: Full-Time

Shift: Day Job

Travel: Yes - 10% of the time

Minimum Clearance Required: Secret

Clearance Level Must Be Able to Obtain: None

Potential for Remote Work: ORA_HYBRID


Description

SAIC is seeking a talented and experienced Machine Learning Developer to join our dynamic team.

This position is hybrid in Arlington, VA with 2-3 days per week onsite at the Pentagon or the Mark Center.

The ideal candidate will have a strong background in computer science, software engineering, and experience with machine learning algorithms and frameworks. The Machine Learning Developer will collaborate with software engineers to create innovative ML/AI solutions, improve predictive models, and deploy machine learning systems into production.

Key Responsibilities:

  • Develop and implement machine learning models and algorithms to provide suggested values to readiness reports for our DOD client.
  • Refine data collection processes and improve data quality.
  • Design and develop scalable machine learning solutions for various applications.
  • Work with software developers to integrate machine learning models into production systems.
  • Conduct research to identify new approaches and methods for machine learning and AI.
  • Stay updated with the latest trends and advancements in machine learning and AI.
  • Document processes, codes, and workflows for future reference and reproducibility.
  • Provide support and maintenance for deployed machine learning systems. 

Qualifications

Required Education:

  • Bachelors and five (5) years or more experience; Masters and three (3) years or more experience; PhD and zero (0) years related experience; four (4) years of experience considered in lieu of degree.

Qualifications:

  • Proven experience designing, developing, and deploying OpenAI solutions as a Machine Learning Developer or in a similar role.
  • Strong programming skills in Python, R, C#, Java or similar languages.
  • Experience with deep learning techniques and models.
  • Expertise in natural language processing (NLP) or computer vision.
  • Proficiency with machine learning frameworks and libraries such as TensorFlow, PyTorch, Scikit-Learn, etc.
  • Experience with data preprocessing, data mining, and data visualization techniques.
  • Strong analytical and problem-solving skills.
  • Excellent communication and teamwork abilities.
  • Familiarity with software development best practices and source control (e.g., Git).

Clearance:

  • Active Secret clearance is required for this position.

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