Ishita Bindlish
Senior Software Engineer at Weights & Biases
Seattle, Washington, United States
Summary
🤩
Rockstar🎓
Top SchoolIshita Bindlish is a Senior Software Engineer based in Seattle with 10 years of experience blending machine learning and full-stack engineering, currently advancing ML developer tooling at Weights & Biases. She transitioned from three years in data science and ML roles at TSYS and Visa into production software, bringing strengths in feature engineering, anomaly detection, and AI pipeline design. At W&B she has contributed to core artifact management and model workflow features in the widely used wandb repo, shipping backend API work and front-end improvements that help teams move models from experimentation to production. A Georgia Tech MS Analytics graduate and IIIT‑Hyderabad computer science alumna, she combines rigorous analytics training with hands-on product delivery. She’s motivated by measurable impact and enjoys seeing end users benefit directly from her work.
10 years of coding experience
6 years of employment as a software developer
Master of Science - MS Analytics, Master of Science - MS Analytics at Georgia Institute of TechnologyBachelor of Technology Computer Science, Bachelor of Technology Computer Science at International Institute of Information Technology Hyderabad (IIITH)
Github Skills (12)
machine-learning10
api10
artifact10
apidoc10
artifacts10
python10
model-registry10
artifactory10
ml10
data-science9
ai9
collaboration8
Programming languages (4)
Github contributions (5)
wandb/wandb
Sep 2022 - Jan 2023
The AI developer platform. Use Weights & Biases to train and fine-tune models, and manage models from experimentation to production.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:314 reviews, 54 commits, 95 PRs in 4 months
Contributions summary:Ishita made several contributions to the Weights & Biases (wandb) repository, primarily focusing on artifact management features. They implemented the ability to link artifacts to registered models and collections. This included adding an environment variable for configuring batch sizes for download URLs. Moreover, the user worked on expanding the API to manage artifacts, including changes for artifact collections and file upload progress. Additionally, they contributed to the front-end by implementing model workflow features and adding documentation updates.
pythoncollaborationtensorflowhyperparameter-tuningcli
ibindlish/wandb
Aug 2022 - Sep 2022
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Contributions:7 pushes in 2 days
visualizationapipythondata-sciencedeep-learning
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