Deepak Gopinath
Member Of Technical Staff at Thinking Machines Lab
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolDeepak Gopinath is a Member of Technical Staff with 14 years of AI and software engineering experience, currently at Thinking Machines Lab after leading pretraining, post-training, and evaluation efforts for Apple’s Foundation Models that power Apple Intelligence. He has deep expertise in multilingual large language models, data quality, and training recipes, and previously built production neural MT systems and low-resource translation workflows at Meta AI. His research-engineering background spans computer vision and character animation (multiple SIGGRAPH papers) as well as foundational contributions to open-source projects like PyTorch Translate and Caffe2. Comfortable bridging research and production, he’s delivered both novel papers and pragmatic engineering fixes—ranging from seq2seq model systems to GPU-optimized operators—that enabled practical deployments at scale.
14 years of coding experience
8 years of employment as a software developer
BITS Pilani, Birla Institute of Technology and ScienceMaster’s Degree Computational Data Science, Master’s Degree Computational Data Science at Carnegie Mellon University
English, Kannada, Hindi
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (15)
pytorch10
machine-learning10
rnn-model10
nlp10
deep-learning10
caffe10
artificial-intelligence10
n10
python10
fairseq10
transformer9
cuda8
gpu8
onnx8
ai7
Programming languages (5)
Github contributions (5)
pytorch/translate
Jun 2018 - Jun 2019
Translate - a PyTorch Language Library
Role in this project:
ML Engineer
ML EngineerContributions:18 commits, 18 PRs in 11 months
Contributions summary:Deepak primarily focused on enhancing the PyTorch-based machine translation library. Their work included initializing training state variables, integrating support for ensembled generation using a colon-separated path variable, and setting up the semi-supervised task. Furthermore, the user made contributions to the multilingual task and the HybridTransformerRNNModel refactoring. These contributions suggest a strong involvement in model training, data handling, and generation processes within the project.
pytorchnlptranslationmachine-learningonnx
facebookarchive/caffe2
Feb 2017 - May 2017
Caffe2 is a lightweight, modular, and scalable deep learning framework.
Role in this project:ML Engineer
ML EngineerContributions:14 commits in 3 months
Contributions summary:Deepak contributed to the Caffe2 deep learning framework by implementing a fix to handle odd-length arrays within the `RandGaussian` function, which is used by several operators. They also implemented an open-source seq2seq model, removing dependencies and addressing potential GPU acceleration. Furthermore, the user addressed issues related to vocabulary usage in evaluations and optimized data-parallel model gradients, highlighting their work in both operator implementation and model development.
pytorchscalablecaffe2deep-learningml
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