Carnegie Mellon University

Research Scientist

• Execute tasks within the mission context, considering users, use cases, operational constraints, and intended outcomes.
• Translate sponsor goals into clear technical questions, measurable success criteria, and credible evaluation evidence.
• Design and conduct studies grounded in mission needs; form hypotheses, run controlled experiments, analyze results, and produce actionable recommendations.
• Build research prototypes, evaluation harnesses, and reference implementations that demonstrate feasibility and generate learning in realistic settings.
• Develop and apply evaluation methodologies for ML systems (especially CV and LLMs), including metrics, benchmark design, robustness testing, uncertainty and calibration approaches, and repeatable test pipelines.
• Write clear, maintainable code and documentation with a level of engineering discipline proportionate to the intended use.
• Plan and deliver work in iterative cycles; manage priorities effectively; communicate status and risks early; and maintain momentum with minimal supervision.
• Communicate technical progress and results clearly to technical and non-technical stakeholders through briefings, demos, reports, and recommendations.
• Identify opportunities to publish research insights and lessons learned at reputable venues, subject to customer and releasability constraints.
• Contribute to technical discussions shaping tasking and delegation, support shared project goals, and provide guidance to junior teammates when appropriate.

Machine Learning Research Scientist – Frontier Lab

Machine Learning Engineer

• As an Machine Learning Engineer, you will specialize in engineering solutions that support research into the vulnerabilities of AI and ML algorithms and securing against those vulnerabilities.
• Identifying and investigating emerging AI and AI-adjacent technologies.
• Defining and refining processes, practices, and tools for working with AI.
• Designing and building well-engineered prototypes of AI systems.
• Transitioning and providing guidance on AI capabilities to government sponsors.
• Building Machine Learning Models and Systems: You will work with machine learning frameworks such as TensorFlow, PyTorch, Torch, and Caffe and modern programming languages including Python, C/C++, and Java.
• Conducting rapid prototyping to demonstrate and evaluate technologies in relevant environments.
• Evaluating systems for performance and security.
• Actively participating on teams of developers, researchers, designers, and technical leads.
• Mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI.

Machine Learning Engineer – Secure AI Lab

• Conduct systematic media searches and content analysis of news articles, reports, and public filings
• Build rigorous case dossiers for each incident: timeline, actors, technologies used, legal/regulatory context
• Perform secondary research on workplace-monitoring vendors and firms
• Review and flesh out incoming cases self-reported by workers
• Update an online tracker with new cases and changes to existing cases
• Create concise case summaries and metadata for publication
• Support the project lead with literature scans and citation management

Research Assistant, Computer Science

• Following research plans to collect, record, and analyze research data.
• Maintains accurate and detailed research records and files.
• Testing software prototypes and interacting with fellow researchers.
• Performing basic data analysis to produce reports and contribute to research publications.
• Other duties as assigned.

Machine Learning Research Scientist – Frontier Lab

Machine Learning Engineer – Secure AI Lab

Research Assistant, Computer Science

Research Assistant – Chemical Engineering