Leads and scales an engineering team building NVIDIA’s deep learning inference software (vLLM, SGLang, FlashInfer) for GPU-accelerated AI model deployment. Drives roadmap, performance optimization, and cross-team collaboration for LLMs and generative AI on NVIDIA GPUs using CUDA, Triton, and NCCL.
Designs and scales NVIDIA's automated inference and deployment platform for generative AI models such as LLMs and diffusion models. The role optimizes inference efficiency using PyTorch and HuggingFace model graphs, high-performance CUDA/TRT-LLM/Triton kernels, and techniques like model sharding and KV-caching within the TRT Model Optimizer stack.
Owns end-to-end computer vision projects for power-grid infrastructure, taking ML research from paper to production-deployed models for defect detection, thermal-anomaly identification, and vegetation monitoring. Day-to-day work uses Python, PyTorch/Lightning, OpenCV, and ML DevOps tooling within a remote U.S. team.
The Machine Learning Engineer trains, tunes, and evaluates large language models for healthcare use cases using techniques such as SFT, PEFT, DPO, PPO, and RAG, collaborating with engineers, data scientists, and medical professionals. Core technologies include PyTorch, Axolotl, vLLM, TGI, and llama-cpp.
Required Skills: A master’s degree or Ph.D. in Artificial Intelligence, Machine Learning, Computer Science, or a related field, along with demonstrated experience working on or developing AI/ML capabilities. At least…
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[ Quora is a privately held, "remote-first" company . This position can be performed remotely from anywhere in Canada or the United States. Please visit for details regarding employment eligibility by country.]…
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