Machine Learning / Federated-Learning Engineer

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

Overview

We are seeking a Machine Learning (ML) / Federated-Learning Engineer responsible for developing, implementing, and supporting machine learning solutions within controlled and distributed environments. This role will support the Bounded Use Case B demonstration through controlled model adaptation, fine-tuning, and federated learning workflows.

The ML / Federated-Learning Engineer will work across the machine learning lifecycle to develop and integrate model training and adaptation workflows, support distributed and federated learning capabilities, evaluate model performance, and ensure solutions operate within defined technical and security constraints. This role requires strong hands-on experience with machine learning engineering, model development, and distributed computing environments.

Responsibilities

• Design, develop, and implement machine learning solutions supporting the Bounded Use Case B demonstration
• Develop and execute controlled model adaptation and fine-tuning workflows based on defined use cases and requirements
• Design, implement, and support federated learning workflows that enable distributed model training and adaptation
• Develop and maintain machine learning pipelines supporting data preparation, model training, fine-tuning, evaluation, and deployment
• Analyze and preprocess data, including feature engineering and transformation, to support machine learning workflows
• Configure and optimize machine learning models and training processes to meet defined performance and operational requirements
• Evaluate model performance, behavior, and effectiveness using established metrics and validation techniques
• Develop processes and controls to ensure model adaptation and training occur within defined technical, security, and operational boundaries
• Integrate machine learning capabilities with existing applications, platforms, data sources, and infrastructure
• Troubleshoot model training, integration, performance, and distributed learning issues
• Develop reusable code, tools, and automation to support machine learning and federated learning workflows
• Collaborate with data scientists, software engineers, cloud engineers, cybersecurity teams, and other technical stakeholders to develop and integrate machine learning capabilities
• Document machine learning architectures, workflows, configurations, testing results, and technical implementation decisions
• Support version control, CI/CD, and other software engineering practices throughout the machine learning development lifecycle
• Support an Agile software development lifecycle
• Maintain awareness of emerging machine learning, model fine-tuning, federated learning, and distributed AI technologies and practices

Qualifications

Required:

• Ability to obtain and maintain a government security clearance
• Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, or a related technical field, or equivalent relevant experience
• 5+ years of experience in software engineering, data science, machine learning, AI engineering, or related technical disciplines, including hands-on machine learning engineering experience
• Hands-on experience developing, training, fine-tuning, and evaluating machine learning models
• Experience designing and implementing machine learning training and inference workflows
• Experience with federated learning, distributed machine learning, or distributed model training concepts and architectures
• Strong programming experience using Python and common machine-learning libraries and frameworks
• Experience with machine learning frameworks such as PyTorch, TensorFlow, scikit-learn, or equivalent technologies
• Experience with data preprocessing, feature engineering, and model evaluation techniques
• Experience developing and maintaining data and machine-learning pipelines
• Knowledge of model evaluation techniques, performance metrics, and validation methodologies
• Experience integrating machine learning models and capabilities into applications or production environments
• Understanding of distributed computing concepts and architectures
• Knowledge of cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP)
• Experience with version control systems such as Git and CI/CD practices
• Experience troubleshooting machine learning model, pipeline, and integration issues
• Strong analytical, problem-solving, communication, and collaboration skills

Preferred:

• Hands-on experience implementing federated learning architectures or workflows
• Experience with federated learning frameworks or technologies
• Experience with large language models (LLMs), foundation models, or other generative AI technologies
• Experience with parameter-efficient fine-tuning or other model adaptation techniques
• Experience deploying and operating machine learning workloads in cloud environments
• Knowledge of MLOps practices, model lifecycle management, and automated ML pipelines
• Experience implementing machine learning solutions within controlled, secure, or restricted environments
• Experience working within federal government or other highly regulated environments
• Relevant cloud, machine learning, or AI certification