About this role
Job Summary: In this role, you'll apply your expertise to help train next-generation AI systems. Your work will shape how models learn, reason, and perform through high-quality, real-world input.
Skills
Computational PhysicsScientific ComputingPythonC++MatlabRootNumerical SimulationsMonte Carlo MethodsHpc WorkflowsData AnalysisQuantitative ModelingScientific Data InterpretationAnalytical ReasoningMathematical ReasoningScientific CommunicationWritten CommunicationVerbal CommunicationTeam CollaborationScientific IntegrityAi/ml WorkflowsScientific Benchmarking
Key responsibilities
- Leverage deep expertise in computational physics to contribute high-quality insights to AI evaluation projects.
- Analyze, interpret, and synthesize large-scale scientific data sets and experimental results.
- Develop, review, and refine quantitative models, simulations, or workflows in relevant physics domains.
- Collaborate with interdisciplinary teams to inform and improve AI model capabilities in scientific reasoning.
- Apply advanced numerical methods and computational techniques to real-world physics challenges.
- Document findings and communicate complex concepts clearly in both written and verbal formats.
- Maintain high standards of scientific integrity and accuracy in all project deliverables.
Required skills & qualifications
- PhD in physics or a closely related field, or equivalent industry/research experience.
- Expertise in computational physics, scientific computing, or simulation-heavy research is desirable.
- Proficiency with Python, C++, MATLAB, and scientific tools such as ROOT would be ideal.
- Experience working with numerical simulations, Monte Carlo methods, or HPC workflows.
- Strong analytical, quantitative, and mathematical reasoning abilities.
- Demonstrated experience in experimental or scientific data analysis.
- Excellent written and verbal communication skills; ability to convey complex topics to diverse audiences.
Preferred qualifications
- Background in particle physics, astrophysics, plasma/fusion, or quantum systems.
- Experience with AI/ML workflows, scientific benchmarking, or evaluation practices.
- Track record of published research or contributions to scientific literature.
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