Federated ML Engineer: Distributed Training & Fine-Tuning

UNAVAILABLE; McLean (VA); On-site; USD 120,000 - 210,000; Full time

Job summary

N/A is seeking a Machine Learning (ML) / Federated-Learning Engineer to develop and support ML solutions in controlled, distributed environments. You will drive model training, adaptation, and federated workflows across the ML lifecycle, ensuring compliance with security constraints and integration with existing platforms.

The role requires hands-on ML engineering experience, strong Python and ML framework skills, and ability to collaborate with data scientists, cloud engineers, and

Qualifications

• Bachelor’s degree or equivalent technical experience.
• 5+ years in software/data/ML engineering with hands-on ML work.
• Experience developing training, fine-tuning, and evaluating models.
• Familiar with federated learning and distributed training concepts.
• Strong Python and ML framework proficiency (PyTorch, TF, scikit-learn).
• Experience with data preprocessing, pipelines, and deployment.
• Knowledge of cloud platforms (AWS/Azure/GCP) and Git/CI-CD.

Responsibilities

• Design, develop, and implement ML solutions for the Bounded Use Case B demo.
• Create controlled model adaptation and fine-tuning workflows.
• Develop and support federated learning workflows for distributed training.
• Build ML pipelines: data prep, training, evaluation, deployment.
• Analyze data, perform feature engineering, and optimize models.
• Ensure training and adaptation stay within security and operational boundaries.
• Integrate ML capabilities with existing apps and infrastructure.
• Troubleshoot training, integration, and distributed learning issues.
• Collaborate with data scientists, software/cloud engineers, and cybersecurity teams.
• Document architectures, workflows, tests, and implementation decisions.

Skills

Strong analytical skills

Problem-solving

Communication

Collaboration

Security clearance (govt)

Education

Bachelor’s degree in a technical field or equivalent experience

Tools

Python

PyTorch

TensorFlow

scikit-learn

Git

CI/CD

AWS

Azure

GCP

Distributed computing

Job description

N/A is seeking a Machine Learning (ML) / Federated-Learning Engineer to develop and support ML solutions in controlled, distributed environments. You will drive model training, adaptation, and federated workflows across the ML lifecycle, ensuring compliance with security constraints and integration with existing platforms.

The role requires hands-on ML engineering experience, strong Python and ML framework skills, and ability to collaborate with data scientists, cloud engineers, and