
Machine Learning Research Scientist – Frontier Lab
Carnegie Mellon University
full-time
Posted on:
Location Type: Hybrid
Location: Pittsburgh • Pennsylvania • Virginia • United States
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Tech Stack
About the role
- 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
Requirements
- 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
Benefits
- comprehensive medical, prescription, dental, and vision insurance
- a generous retirement savings program with employer contributions
- tuition benefits
- ample paid time off and observed holidays
- life and accidental death and disability insurance
- a free Pittsburgh Regional Transit bus pass
- access to our Family Concierge Team to help navigate childcare needs
- fitness center access
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningstatistical learningexperiment designevaluation methodologiesPythonPyTorchTensorFlowmetricsbenchmark designrobustness testing
Soft Skills
communicationself-directionprioritizationproblem-solvingcollaborationtechnical writingpresentation skillsadaptabilitymentorshipproject management