• Improved VLM inference latency/throughput by 1.6x–2.2x across 3 customer workloads — helping land $_M ARR in new revenue

• Improved TTFT of Kimi K2.5 by 11%–21% for 5–10 image case by writing a custom position embedding bicubic interpolation CUDA kernel — outperforming torch.nn.functional.interpolate by 100x–1700x

• Improved MXFP8 CUTLASS GEMM performance by 10–40% at batch ≤32 via swap ab trick, increasing system-wide tok/s by 4–5% across ___B+ annual output tokens

Contributor to NVIDIA (FlashInfer, CUTLASS) and Meta (PyTorch) open-source projects:

• PyTorch: Rewrote CUDA upsample_bicubic2d kernel to parallelize across batch and channel dimensions — 4.3-43x speedup for VLM position embedding resizing - https://github.com/pytorch/pytorch/pull/174578

• FlashInfer: Upstreamed FP8 Groupwise GEMM optimization for small-M decode shapes — 10-40% faster at batch ≤32 - https://github.com/flashinfer-ai/flashinfer/pull/2327

• CUTLASS: Fixed SM100 (Blackwell) FP8 profiler bug — corrected epilogue shape_div divisibility condition for non-multiple-of-64 N tiles - https://github.com/NVIDIA/cutlass/pull/2946

• Designed and built fraud response service over gRPC, MySQL, DynamoDB, AWS — handling 10–100x projected growth in fraud cases

• 7th most active contributor (as of 19 Jan 2024) to official Meta AI projects — Llama-stack/Llama-recipes. Full list of merged PRs: https://bit.ly/4kLbRQm

Developed and maintained enterprise application managing $800M+ in research funding allocation for Australia's main medical research organization