Create rigorous mechanical engineering problems and verified worked solutions, then test and refine them against advanced AI models. Evaluate AI-generated engineering work for accuracy and practical applicability, document technical failures, compare solutions, and review other contributors’ work.
Create realistic engineering problems and evaluate AI-generated solutions against professional engineering standards. Compare model versions, critique technical outputs, and document evidence of errors across circuit and electronics disciplines.
Design and evaluate complex data science tasks to train and improve frontier AI models. Document model performance, write grading rubrics, and identify technical failures in AI-generated outputs.
Lead the design and integration of electrical infrastructure for hyperscale data centers, focusing on onsite power generation systems. Manage the full project lifecycle from concept development through construction, commissioning, and operational readiness.
Design and verify challenging mechanical engineering problems to test and improve AI model reasoning capabilities. Evaluate AI-generated technical documentation and engineering designs for accuracy, identifying errors and potential failure modes.
Experts will design and evaluate technical engineering tasks to train and improve AI models on real-world circuit and electronics problems. Responsibilities include writing grading rubrics, identifying technical errors in model outputs, and verifying the accuracy of engineering simulations and schematics.
Evaluate and improve AI-generated content by applying deep scientific expertise in life sciences. Contribute specialized knowledge to the development of high-quality, scientifically accurate AI systems.
Selected experts will evaluate and improve AI-generated content by applying deep scientific expertise to ensure accuracy. They will contribute to the development of high-quality AI systems through a 12-week training sprint.
