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.

Contributions

• 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

About steampunk

Steampunk relies on several factors to determine salary, including but not limited to geographic location, contractual requirements, education, knowledge, skills, competencies, and experience. The projected compensation range for this position is $115,000 to $150,000. The estimate displayed represents a typical annual salary range for this position. Annual salary is just one aspect of Steampunk's total compensation package for employees. Learn more about additional Steampunk benefits here.

Identity Statement

As part of the application process, you are expected to be on camera during interviews and assessments. We reserve the right to take your picture to verify your identity and prevent fraud.