Federated ML Engineer - Distributed Training & Fine-Tuning

Steampunk; McLean (VA); On-site; USD 115,000 - 150,000; Full time

Job summary

Steampunk seeks a Machine Learning (ML) / Federated-Learning Engineer to design, implement, and support ML solutions within controlled and distributed environments for the Bounded Use Case B demonstration.

The role involves building training, fine-tuning and federated learning workflows, evaluating models, and ensuring security and operational constraints across platforms. Collaborative work with data scientists, cloud engineers and cybersecurity teams is essential.

Qualifications

• Bachelor’s degree in a technical field or equivalent experience.
• 5+ years of hands-on software engineering, data science or ML experience.
• Experience with federated learning and distributed model training.
• Proficiency with Python and ML libraries/frameworks.
• Ability to obtain/maintain a government security clearance.

Responsibilities

• Design, develop and implement ML solutions for the Bounded Use Case B demonstration.
• Develop controlled model adaptation and fine-tuning workflows.
• Design, implement and support federated learning workflows for distributed training.
• Build ML pipelines for data prep, training, evaluation and deployment.
• Analyze data, perform feature engineering and preprocessing.
• Configure and optimize models and training to meet performance/operational needs.
• Evaluate model performance with established metrics/validation techniques.
• Ensure training/adaptation occurs within defined security and technical boundaries.
• Integrate ML capabilities with existing apps, platforms and data sources.
• Troubleshoot training, integration and distributed learning issues.
• Develop reusable code/tools and automation for ML workflows.
• Collaborate with data scientists, software/cloud/cybersecurity teams.
• Document architectures, workflows, tests and decisions.
• Support version control, CI/CD and Agile processes.
• Stay updated on evolving ML, federated learning and distributed AI.

Skills

Security clearance eligibility

Analytical thinking

Communication skills

Collaboration

Education

Bachelor's degree in Computer Science or related field

Tools

Python

PyTorch

TensorFlow

scikit-learn

Git

CI/CD

AWS

Azure

GCP

Distributed computing

Job description

Steampunk seeks a Machine Learning (ML) / Federated-Learning Engineer to design, implement, and support ML solutions within controlled and distributed environments for the Bounded Use Case B demonstration.

The role involves building training, fine-tuning and federated learning workflows, evaluating models, and ensuring security and operational constraints across platforms. Collaborative work with data scientists, cloud engineers and cybersecurity teams is essential.