Federated ML Engineer: Secure Distributed Model Training

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

Benefits offered by this job

Health insurance

Job summary

Steampunk, Inc. seeks a Machine Learning (ML) / Federated-Learning Engineer to develop and support ML solutions in controlled, distributed environments.

This role focuses on model adaptation, fine-tuning, and federated workflows for the Bounded Use Case B demonstration. The engineer will work across the ML lifecycle, develop training/adaptation workflows, evaluate performance, and ensure operations stay within defined technical and security constraints.

Qualifications

• Bachelor’s degree in CS, Data Science, AI, ML, Engineering, Mathematics, or related field, or equivalent experience.
• 5+ years in software engineering, data science, ML, AI engineering, or related disciplines with hands-on ML
• Hands-on experience developing, training, fine-tuning, and evaluating ML models
• Experience designing ML training and inference workflows
• Experience with federated learning and distributed ML architectures
• Strong Python and ML libraries (PyTorch, TensorFlow, scikit-learn)
• Experience with data preprocessing, feature engineering, and model evaluation
• Experience building and maintaining ML pipelines
• Knowledge of model evaluation techniques, metrics, and validation
• Experience integrating ML models into applications or production environments
• Understanding of distributed computing and cloud platforms
• Experience with Git and CI/CD practices
• Excellent analytical, problem-solving, and collaboration skills

Responsibilities

• Design, develop, and implement ML solutions for the Bounded Use Case B demo
• Develop controlled model adaptation and fine-tuning workflows
• Design and support federated learning workflows for distributed training
• Build ML pipelines for data prep, training, evaluation, and deployment
• Analyze and preprocess data, including feature engineering
• Configure and optimize ML models to meet performance and security constraints
• Evaluate model performance using established metrics and validation techniques
• Ensure training occurs within defined technical, security, and operational boundaries
• Integrate ML capabilities with applications, platforms, data sources, and infra
• Troubleshoot training, integration, and distributed learning issues
• Develop reusable code, tools, and automation for ML workflows
• Collaborate with data scientists, software/cloud engineers, cybersecurity teams
• Document architectures, workflows, configurations, and decisions
• Support CI/CD and Agile software practices throughout ML lifecycle
• Maintain awareness of advancements in ML, fine-tuning, and distributed AI

Skills

Python

PyTorch/TensorFlow

Federated learning

Distributed computing

Cloud platforms (AWS/Azure/GCP)

Git

CI/CD

Education

Bachelor's degree in Computer Science, Data Science, AI/ML, Engineering, Mathematics or related field

Tools

Git

CI/CD pipelines

Job description

Steampunk, Inc. seeks a Machine Learning (ML) / Federated-Learning Engineer to develop and support ML solutions in controlled, distributed environments.

This role focuses on model adaptation, fine-tuning, and federated workflows for the Bounded Use Case B demonstration. The engineer will work across the ML lifecycle, develop training/adaptation workflows, evaluate performance, and ensure operations stay within defined technical and security constraints.