Carnegie Mellon University

Research Scientist

• Execute work within the operational context—understanding users, workflows, constraints, success criteria, and outcomes
• Lead technical execution by defining technical tasking, sequencing work into realistic milestones, maintaining delivery quality, and delegating appropriately across the team
• Design and run studies, build convincing prototypes and reference implementations, and produce evidence-backed insights that can be matured and transitioned into operational settings
• Establish credible evaluation strategies and test pipelines that assess performance, robustness, reliability, and trustworthiness in mission-representative scenarios
• Serve as the primary technical interface when appropriate; translate mission goals into measurable technical outcomes; communicate progress, decisions, and risks clearly to stakeholders
• Proactively mentor junior staff and teammates, raising the bar for research rigor, engineering practice, and delivery habits across project teams

Senior Machine Learning Research Scientist – Frontier Lab

Machine Learning Engineer

• 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: work with frameworks such as TensorFlow, PyTorch, Torch, and Caffe; modern programming languages including Python, C/C++, and Java; build and work with data pipelines, ETL processes, and backend systems; work with, extend, and implement state-of-the-art machine learning methods
• Technical Experimentation: experiment with modern and emerging machine learning frameworks, methods, and algorithms in applications such as computer vision, natural language processing, planning and scheduling, and robot control; conduct rapid prototyping to demonstrate and evaluate technologies in relevant environments; evaluate systems for performance and security; test capabilities using novel testing and analysis techniques
• Collaboration: actively participate on teams of developers, researchers, designers, and technical leads; collaborate with researchers and government customers to understand challenges, needs, and possible solutions
• Mentoring: contribute to improving the overall technical capabilities of the Division by mentoring and teaching others, participating in design sessions, and sharing insights and wisdom across the SEI

Senior Machine Learning Engineer

• Identify, lead, and conduct research in support of critical U.S. government needs
• Lead novel research in applied machine learning and robotics
• Lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders
• Actively participate with researchers, developers, engineers, designers, technical leads, campus labs, and our government customers to understand challenges, needs, and potential solutions
• Mentor and teach junior staff, lead technical sessions, and contribute to the establishment of technical norms for the Lab
• Maintain awareness of the latest in AI-enabled autonomous systems, especially as it pertains to National Security use cases
• Develop and deliver compelling technical presentations for diverse audiences, tailor content for varied stakeholders, speak at conferences and workshops, and publish in pertinent peer-reviewed journals

Senior Autonomous Systems Research Scientist

• specialize in conducting research into the vulnerabilities of AI and ML algorithms and securing against those vulnerabilities
• conduct and lead novel research in applied machine learning and artificial intelligence
• work with and lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders
• work with the leadership team and colleagues to plan, develop, and carry out an overall research strategy
• actively participate on teams of software developers, researchers, designers, and technical leads
• build relationships and collaborate with researchers, government customers, and other stakeholders
• contribute to improving the overall technical capabilities of the Division by mentoring and teaching others

Senior Machine Learning Research Scientist

Engineer

• identify, shape, apply, conduct, and lead engineering research
• work with and lead interdisciplinary teams to turn research results into prototype capabilities
• conduct and lead novel prototyping in applied artificial intelligence
• work with AI Division leaders to plan overall research and engineering strategy
• actively participate on teams of software developers, researchers, and designers
• mentor and teach others to improve overall technical capabilities

Machine Engineer – Autonomy Lab

• 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 (e.g., NeurIPS , ICLR, MLCON, etc.), 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

• Reverse engineer malicious code in support of high-impact customers
• design and develop new analysis methods and tools
• work to identify and address emerging and complex threats
• effectively participate in the broader security community
• Perform in-depth reverse engineering of malicious code, document and transition results in reports, presentations, and technical exchanges
• Explore ways to use artificial intelligence to support reverse engineering applications

Senior Reverse Engineer, Researcher

Full-stack Engineer

• Perform embedded software systems analysis
• Develop strategic partnerships with customers located in Hill AFB, UT
• Assist customers with software development and cybersecurity efforts
• Provide on-the-spot technical advice to the customer contractor software development team
• Address known issues and communicate progress
• Assess credibility of software development plans for future performance
• Assist in the proper application of tools, techniques, and procedures to solve difficult problems in software engineering and cybersecurity
• Analyze software architecture in terms of functional and non-functional quality attributes
• Analyze use of modern software development techniques e.g., DevSecOps
• Analyze use of cyber security approaches, methodologies, tools, and techniques
• Use static, dynamic and complexity analysis software tools to identify areas of weakness in software