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.

Design, implement, review, and validate production-grade Rust backend systems for AI training projects. Responsibilities include API development, persistence, asynchronous processing, concurrency, performance optimization, reliability testing, architecture assessment, debugging, and creating reproducible environments with databases, dependencies, fixtures, and containers. The role requires clear technical documentation, evaluation of failure scenarios and race conditions, and consistent delivery of high-quality implementation artifacts.