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Unconventional AI

Unconventional AI
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AI Systems, Model OptimizationUnconventional AI
Remote — United States
An MS/PhD or equivalent research/project experience in a quantitative field such as AI/Machine Learning, Computer Science, Physics, Electrical Engineering, or Applied Math.
Deep, practical understanding of the modern AI/ML stack and optimized compilation and execution of algorithms on modern GPU systems. Proven experience in profiling, identifying, and resolving performance bottlenecks in complex ML codebases.Systems Fluency: Demonstrated ability to map state-of-the-art AI model architectures (e.g., Transformers, Mixture of Experts, diffusion models) to system performance implications and apply advanced efficiency techniques such as sparsity, quantization, and distillation.Software Development: Deep experience with PyTorch, including its internals, torch.compile, and distributed data parallel (DDP) / fully sharded data parallel (FSDP) libraries.
Deadline:27 Nov 2026