Rahul Rambhatla
Development Manager at Fervo Energy
Houston, Texas, United States
Summary
🤩
Rockstar🎓
Top SchoolRahul Rambhatla is a Development Manager with a decade of engineering experience who now leads teams building next-generation, 24/7 geothermal power at Fervo Energy. He excels at integrating commercial and technical functions to drive cross-disciplinary projects from concept through execution, drawing on an MBA and a strong background in process and project engineering from ExxonMobil. Rahul has managed large brownfield programs with global teams, introduced digital tools like VR/AR to improve execution, and identified technology opportunities that cut GHG emissions by up to 70%. He pairs hands-on technical contributions—such as ML-focused work on the popular pytorch/torchrec repo improving DLRM examples and efficient Criteo data pipelines—with a penchant for rapid iteration and experimentation. Based in Houston, he’s known for navigating uncertainty, strengthening team culture across continents, and finding novel, commercially viable solutions to complex problems. Colleagues describe him as an “integrator” who bridges disciplines to accelerate delivery while keeping sustainability and practicality front and center.
10 years of coding experience
6 years of employment as a software developer
Master of Business Administration - MBA, Master of Business Administration - MBA at Texas McCombs School of BusinessBachelor’s Degree, Chemical and Biomolecular Engineering, Bachelor’s Degree, Chemical and Biomolecular Engineering at Cornell University
Github Skills (7)
pytorch10
machine-learning10
recommendation-system10
deep-learning10
python10
distributed-training9
cuda9
Programming languages (4)
Github contributions (5)
pytorch/torchrec
Nov 2021 - Dec 2021
Pytorch domain library for recommendation systems
Role in this project:
ML Engineer
ML EngineerContributions:1 review, 13 commits, 2 PRs in 15 days
Contributions summary:Rahul primarily contributes to the `torchrec` library, focusing on the Criteo dataset and DLRM models within a recommendation system context. Their work includes the creation of the `InMemoryBinaryCriteoIterDataPipe` for efficient data loading, refactoring of the model code by removing references to SparseNN, and enhancements to the DLRM example by adding train/val/test splits and evaluation metrics. They also updated the learning rate in the example and addressed issues in the quant library.
cudapytorchrecommendation-systemsdeep-learninggpu
rkindi/mm_bench
Apr 2023 - Feb 2025
Contributions:8 pushes, 1 branch in 1 year 10 months
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