Member of Technical Staff - AI Training Platform

Unconventional AI is developing energy-efficient computing infrastructure for artificial intelligence by exploiting semiconductor physics. The Member of Technical Staff, AI Training Platform will build and scale end-to-end infrastructure for training, evaluating, and benchmarking AI models, including distributed systems, proprietary frameworks, kernel optimization, and hardware evaluation tooling.

Responsibilities:

• Training Infrastructure: Architect, scale, and maintain the core AI/ML/RL training platform and infrastructure. Design and scale multi-node distributed training systems, implementing elastic sharding and robust data streaming pipelines for fast, large-scale iteration. Implement and robust model checkpointing and recovery mechanisms
• Framework Development: Maintain and expand our proprietary training framework, ensuring it provides a robust and flexible foundation for all internal model training
• Optimization & Benchmarking: Develop and optimize kernels using low-level programming models like CUDA and Triton. Design rigorous benchmarking suites to track Model Flops Utilization (MFU), memory bandwidth, and convergence stability
• Hardware Evaluation Tooling: Build and iterate on tooling to enable rapid, high-level evaluation of novel hardware ideas against benchmarks
• Cross-Functional Collaboration: Act as a translator, discussing algorithmic trade-offs with theorists and converting model requirements into concrete specifications for infrastructure and hardware engineering teams
• Optimization & ML Hillclimbing: Design systems to track and visualize quality-vs-efficiency Pareto frontiers, enabling researchers to optimize models for both performance and energy consumption