Jobs at Gramian Consulting Group

Designs challenging competitive data science and machine learning tasks, reproducible execution environments, reference solutions, automated tests, and evaluation criteria. Uses Python, statistical modeling, mathematical reasoning, and debugging to assess AI coding agents, identify data leakage and implementation flaws, validate solution correctness, and document methodologies. Requires a verifiable Kaggle Expert-level profile, strong practical programming skills, and the ability to independently test and explain technical solutions.

Perform aerodynamic and CFD analyses, build meshes and solver-ready cases, define simulation settings, automate workflows with Python, and validate numerical and physical results. Responsibilities include calculating lift and drag, evaluating convergence and mesh quality, diagnosing unstable or invalid simulations, selecting modeling assumptions, and documenting technical methodologies. The role supports an AI training project and requires strong fluid mechanics, aerodynamics, computational analysis, and programming expertise.

Design and evaluate rigorous hardware engineering problems for AI training projects. Develop reference solutions using calculations, simulations, Python scripts, and experimental reasoning. Model and validate hardware systems against physical test data, create verification methodologies, investigate failures, and document reproducible technical solutions. The role requires substantial hardware engineering ownership, strong programming ability, quantitative reasoning, and mandatory hands-on experience with AI Coding Agents.

Lead realistic, multidisciplinary mechanical engineering projects for an AI training program. Responsibilities include defining requirements, constraints, tasks, dependencies, deliverables, and acceptance criteria; coordinating engineering disciplines; evaluating design trade-offs; and incorporating mechanical design, engineering analysis, DFMA, manufacturing, verification, testing, and root-cause analysis into practical workflows.

Develop, validate, debug, and optimize machine learning models, training pipelines, inference systems, and numerical implementations using Python. Build reproducible workflows, diagnose computational issues, create systematic tests, and improve memory usage, throughput, and performance. Review code and solutions, use coding agents in development, document technical findings, and deliver reliable work independently within project deadlines.