Tanmay Das
Back End Developer at Google
Mountain View, California, United States
Summary
🤩
Rockstar🎓
Top SchoolTanmay Das is a backend-focused software engineer with over 10 years of experience building production-quality systems across Google, Capital One, Lexmark, and startups, now based in Mountain View. He has shipped reliability and performance improvements for large-scale products—from Google Vault’s Gmail export optimizations to Nest Camera virtualization and YouTube Supply-Demand Management feature work. At Google he also contributed to TensorFlow Lite Micro, improving quantization for resource-constrained ML inference on embedded targets, showing an aptitude for low-level ML infrastructure. Comfortable across Java, C/C++, Node.js and Python, he pairs pragmatic engineering with documentation and developer tooling to boost team productivity. He has led and mentored small teams, delivered test frameworks that unlocked product launches, and routinely turns complex operational problems into measurable latency and reliability gains. A lifelong learner by his own description, he blends hands-on coding with system design and a knack for making hard-to-test systems predictable.
10 years of coding experience
18 years of employment as a software developer
New Barrackpore Colony Boys High SchoolBachelor of Technology (B.Tech.) Computer Science, Bachelor of Technology (B.Tech.) Computer Science at West Bengal University of Technology, Kolkata
English
Github Skills (11)
kernel10
quantization10
embedded10
machine-learning10
tensorflow-lite10
sys10
kernel-mode10
xtensa10
cprogramming-language9
c-language9
deep-learning8
Programming languages (2)
Github contributions (5)
tensorflow/tflite-micro
Jul 2022 - Jan 2023
Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).
Role in this project:
ML Engineer
ML EngineerContributions:100 reviews, 31 commits, 113 PRs in 6 months
Contributions summary:Tanmay primarily contributed to the Xtensa quantize kernel, adding support for float to int16 conversion. They addressed issues related to the SEANet NN-only benchmark by modifying quantization operations. The user made modifications to the quantize and requantize functions, including specific adaptations for the HIFI5 architecture. They also made improvements by addressing typo fixes and refactoring the quantization code.
signalml-modelslow-powerprocessorsdeployment
tanmaydas82/tflite-micro
Jul 2022 - Jul 2023
TensorFlow Lite for Microcontrollers
Contributions:333 pushes, 122 branches in 1 year
tensorflow-litelitetensorflowmicrocontrollers
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