Carnegie Mellon University · 1 day ago
Machine Learning Research Scientist - Frontier Lab
Arlington, VA
Full-time
Onsite
Senior Level
5+ years expCarnegie Mellon University is a leading institution in applied artificial intelligence research, and they are seeking a Machine Learning Research Scientist for their Frontier Lab. The role involves conducting applied AI/ML research, developing prototypes, and translating government sponsor needs into technical solutions within a mission context.
EducationHigher EducationUniversities
No H1B
Security Clearance RequiredU.S. Citizen Only
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
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
Benefits
Comprehensive medical, prescription, dental, and vision insurance
Generous retirement savings program with employer contributions
Tuition benefits
Ample paid time off
Observed holidays
Life and accidental death and disability insurance
Free Pittsburgh Regional Transit bus pass
Access to our Family Concierge Team to help navigate childcare needs
Fitness center access
Company
Carnegie Mellon University
Carnegie Mellon University is a private research university that offers degrees in technology, arts, and various other fields.
5001-10000 employees
Funding
Current Stage
Late StageTotal Funding
$488.58MKey Investors
ICANNAppalachian Regional CommissioneBPF Foundation
2025-05-29Grant· $0.45M
2024-09-05Grant· $0.5M
2024-08-29Grant· $0.05M
Leadership Team
Angela Blanton
Vice President for Finance and Chief Financial Officer
Amy Coutu
Assistant Vice President, Enterprise Applications
Recent News
Medical Xpress - latest medical and health news stories
2026-01-18
2026-01-16
Bizjournals.com Feed (2025-11-12 15:43:17)
2026-01-16
Company data provided by crunchbase