Software Engineering Institute | Carnegie Mellon University · 11 hours ago
Machine Learning Research Scientist - Frontier Lab - 2024077
Pittsburgh, PA
Full-time
Onsite
Senior Level
5+ years expThe Software Engineering Institute at Carnegie Mellon University is conducting research in applied artificial intelligence and engineering challenges related to AI-enabled systems. As a Machine Learning Research Scientist, you will conduct applied AI/ML research and develop prototype capabilities that inform and improve government workflows.
ComputerCyber SecurityEducationSoftware
No H1B
Security Clearance RequiredU.S. Citizen OnlyHiring Manager
Bill Scully
Responsibilities
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. Emphasize reproducibility and handoff-ready artifacts suitable for downstream integration and operational hardening through formal DevSecOps processes
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
Qualification
Required
BS in Electrical Engineering, Computer Science, Statistics, or related discipline with eight (8) years of experience in hands-on software development; OR MS in the same fields with five (5) years of experience; OR PhD with two (2) years of relevant experience
Strong foundation in machine learning and statistical learning, including experiment design and evaluation
Demonstrated ability to implement ML systems in Python using modern ML libraries (e.g., PyTorch / TensorFlow) and common scientific tooling
Demonstrated ability to communicate technical results clearly in written deliverables and presentations
Ability to work effectively with ambiguity and deliver results in iterative project cycles with strong self-direction
Communication: Explains technical content clearly; translates between mission problems and technical approaches
Scientific rigor: Designs sound experiments; recognizes evaluation pitfalls (leakage, confounds, distribution shift)
Practical execution: Balances research quality with timelines and constraints; produces credible evidence and useful prototypes
Collaboration: Works well in interdisciplinary teams; contributes effectively to shared code and shared evaluation approaches
Autonomy: Executes independently with low oversight; manages time effectively; escalates risks early and seeks guidance when needed
Flexible to travel to SEI offices in Pittsburgh, PA and Washington, DC / Arlington, VA, sponsor sites, conferences, and offsite meetings (~10% travel)
You must be able and willing to work onsite at an SEI office in Pittsburgh, PA or Arlington, VA 5 days per week
You will be subject to a background investigation and must be able to obtain and maintain a Department of War security clearance
Preferred
Applied ML research and prototyping for real operational workflows, including tool-integrated AI systems and human-in-the-loop settings
Designing and operating evaluation pipelines for LLMs and/or CV models (benchmarking, regression testing, robustness checks, scenario-based evaluations)
Language model grounding and reliability techniques (structured knowledge integration, RAG, tool use, error analysis)
Learning under constrained/noisy data conditions (synthetic data, programmatic labeling, semi-/self-supervised learning)
Edge-relevant ML (compression, quantization, distillation, efficient inference, rapid adaptation patterns)
Evidence of research output: publications, technical reports, open-source contributions, or applied research artifacts
Experience working with government/DoW stakeholders or in high-assurance environments
Company
Software Engineering Institute | Carnegie Mellon University
At the SEI, we research complex software engineering, cybersecurity, and AI engineering problems; create and test innovative technologies; and transition maturing solutions into practice.
501-1000 employees
Funding
Current Stage
Late StageLeadership Team
Paul Nielsen
Director and CEO
Christopher Herr
Senior Engineer/Cybersecurity Exercise Developer and Trainer
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