Ben Hammel is a Staff ML Engineer with 11 years of experience blending deep-learning engineering and leadership at fast-moving startups and national labs, currently advancing next-generation AI models in Redwood City. He holds a Ph.D. and postdoc in experimental physics and has a track record of translating ill-defined, research-grade problems into production-grade ML systems—from accelerating X-ray simulations 100x at LLNL to shipping VLMs and ASR on custom hardware at SambaNova. Ben is self-taught across ML, CS, and EE domains and excels at cross-disciplinary collaboration, routinely partnering with compiler, hardware, and production teams to optimize model performance at scale. He’s comfortable both managing teams and acting as an individual contributor, having led multimodal and computer vision groups while also driving hands-on neural network co-design for edge and enterprise deployments. An underrated strength is his ability to operationalize Bayesian and uncertainty-aware methods in real-world inference pipelines, bridging research fidelity and product needs.
12 years of coding experience
17 years of employment as a software developer
Ph. D., Physics, Ph. D., Physics at University of Nevada, Reno
Bachelor of Science, Physics, Bachelor of Science, Physics at UC Santa Barbara
Contributions:48 commits, 12 pushes, 3 branches in 27 days
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