Carnegie Mellon University

Data Engineer

• Monitor and maintain the health and efficiency of data pipelines
• Troubleshoot and perform root cause analysis for data discrepancies and pipeline issues
• Communicate with data providers to understand data discrepancies and manage changes in data delivery
• Implement fixes and enhancements to improve data quality and pipeline performance
• Collaborate with data scientists and analysts to understand data needs and implement effective data solutions
• Develop strategies for data validation and quality assurance
• Optimize data flow and collection to improve system efficiency
• Document and manage data pipeline architectures, including maintenance and update protocols
• Use tools such as SQL, version control and CI/CD, containerization, task schedulers, python frameworks, and cloud services for data pipeline management
• Ensure compliance with data governance and security standards

Data Pipeline Engineer – Computer Science

Data Scientist

• Data Scientists at the SEI use advanced statistics, data analytics, machine learning, and artificial intelligence to help our government and industry clients research and solve cybersecurity challenges.
• In this role, you will work with our customers to identify areas where advanced statistical techniques can help tackle problems, plan and develop prototype solutions, and build out final products.
• You'll get a chance to work with elite cybersecurity professionals and university faculty to build new technologies that will influence national cybersecurity strategy for decades to come.
• You will co-author research proposals, execute studies, and present findings to DoW sponsors and at academic conferences.
• Our team works on a wide range of projects. Our current work includes research in generative AI and large language models, computer vision, multimodal AI, agentic AI, and assurance of AI systems.
• Additionally, we craft metrics and experimental designs for large-scale cybersecurity research programs, develop human-in-the-loop machine learning solutions, and build classifiers to identify security vulnerabilities.
• If you are a data science or statistics expert with an interest in cybersecurity, we want to hear from you!

Assistant Data Scientist

AI Engineer

• Design, develop, and fine-tune a variety of AI models
• Design autonomous agents and multi-step pipelines using LangChain, ReAct, tool-calling, or custom orchestration; employ the Model Context protocol to manage stateful interactions
• Build Retrieval-Augmented Generation pipelines that combine external knowledge bases with LLMs to improve factual accuracy for warfighting applications
• Implement end-to-end data pipelines, ETL processes, and back-end services (Python, C/C++, Java) that feed data to models
• Create CI/CD pipelines for model training, validation, containerized deployment (Docker/Kubernetes), and security scanning; maintain model registries, monitoring, and version control of context protocols
• Produce rapid prototypes, run benchmarks, and conduct robustness/adversarial testing in realistic environments
• Work closely with senior ML engineers, software developers, and government customers; mentor junior staff and contribute to design reviews and documentation
• Stay current with emerging LLM architectures, agentic paradigms, PEFT/LoRA methods, and AI-safety techniques; translate new research into operational capabilities

AI Engineer – Mission Innovation Lab

Security Engineer

• Develop machine learning–based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
• Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
• Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
• Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team

Senior AI Security Software Engineer

• Develop machine learning–based prototypes, tools, and systems for AI security applications
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions
• Apply software engineering best practices to build scalable, maintainable systems
• Process and analyze large, diverse cybersecurity datasets, using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives
• Translate research concepts into practical, operational capabilities

AI Security Software Engineer

• Use advanced statistics, data analytics, machine learning, and artificial intelligence to help clients research and solve cybersecurity challenges
• Identify areas where advanced statistical techniques can help tackle problems
• Plan and develop prototype solutions, and build out final products
• Work with elite cybersecurity professionals and university faculty
• Co-author research proposals and execute studies
• Present findings to DoW sponsors and at academic conferences
• Research in generative AI and large language models, computer vision, multimodal AI, and assurance of AI systems
• Craft metrics and experimental designs for large-scale cybersecurity research programs
• Develop human-in-the-loop machine learning solutions and build classifiers to identify security vulnerabilities

Machine Learning Engineer

• As an Associate 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.

Associate Machine Learning Engineer – Secure AI Lab

• As an Associate 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.
• You will work with machine learning frameworks such as TensorFlow, PyTorch, Torch, and Caffe and modern programming languages including Python, C/C++, and Java.
• Your day-to-day engineering tasks will include: Identifying and investigating emerging AI and AI-adjacent technologies.
• Designing and building well-engineered prototypes of AI systems.
• Transitioning and providing guidance on AI capabilities to government sponsors.
• Experimenting with modern and emerging machine learning frameworks, methods, and algorithms in application domains that include computer vision, natural language processing, planning and scheduling, robot control, and engineering safe, trusted, and reliable machine learning systems.
• You'll evaluate systems for performance and security and conduct rapid prototyping to demonstrate and evaluate technologies in relevant environments.
• You'll actively participate on teams of developers, researchers, designers, and technical leads and collaborate with researchers and our government customers to understand challenges, needs, and possible solutions.

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.

Data Pipeline Engineer – Computer Science

Assistant Data Scientist

AI Engineer – Mission Innovation Lab

Senior AI Security Software Engineer

AI Security Software Engineer

Data Scientist

Associate Machine Learning Engineer – Secure AI Lab

Associate Machine Learning Engineer – Secure AI Lab

Machine Learning Research Scientist – Frontier Lab