Teams at Gramian Consulting Group

Conduct frontier AI research across synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarking, and evaluation. Design experiments, datasets, prototypes, tooling, and evaluation frameworks; train and assess models; analyze results; ensure rigor and reproducibility; and translate findings into scalable AI products. Collaborate across research, engineering, product, and operations teams, communicate technical insights, contribute to publications or open source, and mentor engineers and researchers.

Leads end-to-end delivery of large-scale AI and software-engineering data programs, managing scope, timelines, quality, throughput, contributors, and costs. Designs workflows for coding datasets, agentic trajectories, reinforcement-learning environments, benchmarks, and evaluations. Builds quality-control systems, analyzes operational metrics, manages distributed technical contributors, communicates with AI lab customers, translates research goals into task specifications, automates reporting with code, and develops reusable operational playbooks.

Design reproducible, terminal-based computational mathematics tasks for AI benchmarking. Develop reference solutions, datasets, automated tests, grading criteria, and numerical workflows involving optimization, differential equations, statistics, probability, matrix computation, and modeling. Debug precision, convergence, stability, solver, and performance issues while documenting assumptions, outputs, tolerances, and limitations.