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Research Federated Learning Jobs in Virginia (NOW HIRING)

EchoStar

Senior AI/ML Architect

Herndon, VA

$146K - $210K/yr

Whether through research and development, technology innovation or solution engineering, our team ... Familiarity with advanced AI techniques, including deep reinforcement learning, federated learning ...

BAE Systems

Dir II- Eng

Mclean, VA · On-site

$173K - $294K/yr

... federated innovation network. • Coordinate resource planning closely with the Operations Lead to ... Demonstrated acceleration of R&D timelines without sacrificing rigor, security, or quality. • ...

Lockheed Martin

Cyber Systems Security Engineer

Manassas, VA · On-site

$57.25 - $70.25/hr

At RMS, we foster a culture of Creativity, collaboration, and continuous learning. WHO YOU ARE You ... researching new Cybersecurity capabilities, performing trade studies, and providing expertise to ...

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Research Federated Learning information

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

AspectResearch Federated LearningData Scientist
Required CredentialsAdvanced degrees in CS, ML, or related fields; research experienceBachelor's or Master's in Data Science, Statistics, or related fields
Work EnvironmentResearch labs, tech companies, academia; focus on algorithm developmentBusiness environments, analytics teams; focus on data analysis and insights
Industry UsageAI research, privacy-preserving ML, distributed systemsBusiness intelligence, marketing, finance, healthcare

Research Federated Learning involves developing privacy-focused, distributed machine learning algorithms, often in research or specialized tech settings. Data Scientists analyze data to generate insights and support decision-making in various industries. While both roles require strong analytical skills, Research Federated Learning emphasizes algorithm development and privacy, whereas Data Scientists focus on data analysis and reporting.

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

To thrive as a Researcher in Federated Learning, you need a strong background in machine learning, distributed systems, and statistics, typically supported by an advanced degree in computer science or a related field. Familiarity with programming languages like Python, frameworks such as TensorFlow Federated, and experience with privacy-preserving algorithms are essential. Critical thinking, collaboration, and effective communication are key soft skills for designing experiments and sharing findings with peers. These competencies are vital for advancing privacy-aware AI solutions and producing impactful research in this rapidly evolving domain.

What is a researcher in federated learning?

A Researcher in Federated Learning is a professional who studies, develops, and improves federated learning algorithms and systems. Federated learning is a machine learning approach where data remains decentralized, allowing multiple devices or organizations to collaboratively train models without sharing raw data. These researchers focus on advancing privacy, efficiency, and performance in distributed AI systems. Their work often involves experimenting with new methods, publishing findings, and contributing to the growing field of privacy-preserving machine learning.

What are some common challenges faced by professionals working in research federated learning, and how can they be addressed?

Professionals in Research Federated Learning often encounter challenges such as ensuring data privacy across distributed devices, managing non-iid (non-independent and identically distributed) data, and optimizing communication efficiency between clients and servers. Addressing these issues requires strong collaboration with cross-functional teams, including data engineers, security experts, and software developers, to develop robust protocols and algorithms. Staying updated with the latest research and participating in open-source collaborations can also help overcome technical hurdles and drive innovation in this rapidly evolving field.
What are popular job titles related to Research Federated Learning jobs in Virginia? For Research Federated Learning jobs in Virginia, the most frequently searched job titles are:
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Research Federated Learning jobs near you

AI / ML Subject Matter Expert (SME)

HRC Global Services

Reston, VA • On-site

Full-time

Re-posted 20 days ago

Job description

AI / ML Subject Matter Expert (SME)

Job Title: AI / ML SME – Generative AI & Advanced ML
Location: Remote / Hybrid (U.S.)
Employment Type: Full-Time

About the Opportunity:
Join a cutting-edge team driving AI innovation across enterprise systems. This role focuses on building scalable AI/ML solutions including Generative AI, LLMs, and advanced data pipelines.

Key Responsibilities:

  • Design and deploy AI/ML models including LLMs and RAG-based systems
  • Lead development of intelligent automation solutions
  • Collaborate across teams to build scalable AI platforms
  • Implement ML pipelines, CI/CD, and MLOps frameworks
  • Guide stakeholders on AI adoption and best practices

Required Skills:

  • 5+ years in AI/ML / Data Science / ML Engineering
  • Strong expertise in Python, SQL, and ML frameworks
  • Experience with cloud platforms (AWS / Azure / GCP / Databricks)
  • Hands-on experience with LLMs, RAG, Generative AI
  • Experience with Git, CI/CD, and model deployment

Advanced Requirements:

  • Experience with Hugging Face, LLaMA, LangChain or similar
  • Knowledge of model tuning, experimentation, and evaluation
  • Strong understanding of ML lifecycle and data pipelines

Nice to Have:

  • Experience with Agentic AI (AutoGen, LangGraph, CrewAI)
  • Knowledge graphs / federated learning
  • Research / publications in AI

Clearance: Public Trust (or eligible)

Hashtags:
#AI #MachineLearning #GenerativeAI #LLM #LangChain #Databricks #MLOps #CloudAI #TechLeadership #Hiring