Adit Deshpande
Senior Software Engineer at Verse Medical
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolAdit Deshpande is a Senior Software Engineer based in San Francisco with 10 years of experience building full-stack healthcare products and production ML systems. He has driven core EMR features and operational reliability at Forward, led growth-oriented UX and CMS work at Rula, and now contributes senior engineering at Verse Medical. His hands-on ML background includes building Seq2Seq chatbots and LSTM-based sentiment models, plus practical experience with TensorFlow for CNNs, RNNs and GAN experiments. Comfortable toggling between product-facing frontend work and backend orchestration, he also runs on-call incident response and writes technical specs. Notably, he has combined diverse conversation datasets and embedding pipelines to train conversational agents—evidence of both data engineering rigor and applied research instincts. Trained in computer science at UCLA, he blends startup velocity with machine-learning curiosity to deliver user-centered, reliable systems.
10 years of coding experience
7 years of employment as a software developer
Evergreen Valley High SchoolUniversity of California, Los Angeles
Github Skills (18)
word2vec10
python10
machine-learning10
rnn-model10
n10
mask-rcnn10
sentiment-analysis10
lstm10
deep-learning10
tensorflow10
mnist10
natural-language-processing10
faster-rcnn10
nlp10
chatbot10
Programming languages (7)
Github contributions (5)
adeshpande3/LSTM-Sentiment-Analysis
Jun 2017 - Jun 2018
Sentiment Analysis with LSTMs in Tensorflow
Role in this project:
ML Engineer
ML EngineerContributions:17 commits, 4 PRs, 14 pushes in 1 year
Contributions summary:Adit primarily contributed to the project by adding and modifying code related to a pre-trained LSTM network for sentiment analysis. Their work included creating a notebook to test the pretrained network with custom text inputs. Further contributions involved adding hyperparameter tuning discussions and modifying LSTM stacking explanations. The user also added code to handle unknown words and the correct file decoding.
sentiment-analysistensorflowlstmrnn
adeshpande3/Tensorflow-Programs-and-Tutorials
Dec 2016 - Jun 2018
Implementations of CNNs, RNNs, GANs, etc
Role in this project:ML Engineer & Data Scientist
ML Engineer & Data ScientistContributions:59 commits, 54 pushes, 1 branch in 1 year 6 months
Contributions summary:Adit implemented and experimented with various deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to classify the MNIST dataset. The user was also involved in model design and implemented methods to perform sentiment analysis, and also analyzed the accuracy of these networks with different approaches.
pytorchimplementationsdeep-learningcnnsgenerative-adversarial-network
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