Carnegie Mellon University

Machine Learning Research Scientist – Frontier Lab

Carnegie Mellon University

full-time

Posted on:

Location Type: Hybrid

Location: Arlington • Pennsylvania • Virginia • United States

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Job Level

SeniorLead

Tech Stack

PythonPyTorchTensorflow

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, 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
  • Employee benefits
  • Competitive salary
  • Flexible work arrangements
  • Professional development opportunities
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-directionprioritizationtechnical discussionguidanceproblem-solvingadaptabilitycollaborationanalytical thinkingdocumentation