Bobak Hashemi
Senior Software Engineer -- Machine Learning at Meta
California, United States
Summary
🤩
Rockstar🎓
Top SchoolBobak Hashemi is a Senior Software Engineer specializing in machine learning at Meta, focusing on LLM training and evaluation to boost employee productivity. With a PhD-level physics background and three years in ML roles, he blends rigorous research experience from CERN and FAIR with production-grade engineering. He led development of torcheval, an open-source library for neural network evaluation, and has hands-on experience deploying efficient RNNs and MLOps pipelines in clinical settings. His work spans large-scale data systems, high-performance C++/Python tooling, and novel algorithm development for sensor tracking, reflecting a pattern of turning complex scientific problems into scalable software. Based in California, he brings both deep quantitative instincts and practical product delivery experience to ML infrastructure and model evaluation.
3 years of coding experience
9 years of employment as a software developer
Pennsbury High SchoolDoctor of Philosophy - PhD, Physics, Doctor of Philosophy - PhD, Physics at University of California San DiegoBachelor of Science (B.S.), Physics, Bachelor of Science (B.S.), Physics at Penn State UniversityMaster's degree, Physics, Master's degree, Physics at University of California, San Diego
Persian
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (30)
reinforcement-learning10
computation9
pytorch9
deep-learning9
mujoco7
machine-learning7
code-generation6
abstract-interpretation6
reinforcement-learning-environments6
ocaml6
security6
simulation6
distributed-training6
program-analysis5
physics5
Programming languages (3)
Github contributions (5)
bobakfb/torcheval
Aug 2022 - Dec 2023
A library that contains a rich collection of performant PyTorch model metrics, a simple interface to create new metrics, a toolkit to facilitate metric computation in distributed training and tools for PyTorch model evaluations.
Contributions:2 PRs, 28 pushes, 28 branches in 1 year 4 months
pytorchcomputationcreate-newpytorch-modelevaluations
pytorch/torcheval
Aug 2022 - Jan 2023
A library that contains a rich collection of performant PyTorch model metrics, a simple interface to create new metrics, a toolkit to facilitate metric computation in distributed training and tools for PyTorch model evaluations.
Contributions:1 release, 7 reviews, 28 commits in 5 months
pytorchcomputationcreate-newpytorch-modelevaluations
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