# Shomit D.

> Principal Software Engineer @ Microsoft | AI, FPGA, Compiler

Location: San Francisco Bay Area, United States
Profile: https://flows.cv/shomit

With over 20 years of hands-on experience in software development, I am a Principal Software Engineer at Microsoft, working on low-latency, high-throughput large-scale distributed systems for Bing Search. I leverage my expertise in object-oriented programming, complex algorithms, and data structures to design, implement, and optimize scalable and robust solutions that meet the needs of millions of users worldwide.

I am also passionate about artificial intelligence and data science. I have applied my knowledge and skills in these domains to pioneer the development of native floating point and HDL code generation features for MathWorks and FPGA compiler development for deep learning. I have few patents in these areas, and I enjoy collaborating with other engineers and researchers to advance the state of the art in software engineering.

## Work Experience
### Principal Software Engineer | Bing Ads AI, Reliability & Experimentation @ Microsoft
Jan 2021 – Present | Silicon Valley, California, United States
Led modernization of large-scale distributed systems across Bing Ads, most notably in Campaign Budget Store for high reliability, dual-stream consistency, and faster failover. Latency reductions across pipelines, improving advertiser trust and dramatically reducing budget overspend risk. My work spanned online selection, ranking and index layers - optimizing ranking, filtering and real-time budget enforcement. These contributions strengthened system resilience, experimentation rigor and AI-powered ad delivery across Microsoft Advertising platform.

### Principal Software Engineer @ MathWorks
Jan 2014 – Jan 2021 | Natick, Massachusetts
Pioneered the development of native floating point feature of HDLCoder toolbox from zero. This feature enables floating-point HDL generation directly from MATLAB/Simulink, removing the need for manual-fixed-point conversion. The challenges were consistent numeric behavior with MATLAB simulations (US10936769B2), providing vendor-independent arithmetic, trigonometric and advanced math operations with tunable latency/accuracy, and allowing mixed floating-point + fixed-point design for high-accuracy, area-efficient hardware while preserving IEEE-754 tolerances.
https://www.mathworks.com/help/hdlcoder/ug/native-floating-point-support.html

Spearheaded the development of generating FPGA-ready HDL code from Simscape physical simulation model, performing solver configuration, state-space extraction, implementation model generation and RTL validation for real-time HIL deployment.
https://www.mathworks.com/help/physmod/simscape/ug/generate-hdl-code-using-the-simscape-hdl-workflow-advisor.html

Accelerated FPGA/SoC deployment of Deep learning models by leveraging MathWorks Deep Learning HDL toolbox to generate custom, synthesizable HDL and optimized IP cores, enabling high-throughput inference, design-performance tradeoff analysis, INT8 quantization and hardware-aware profiling on Xilinx/Intel FPGA platforms.
https://www.mathworks.com/products/deep-learning-hdl.html

### Staff Engineer @ Mentor Graphics
Jan 2011 – Jan 2014 | Waltham, Massachusetts, United States
Veloce FPGA backend compiler development addressing new features such as Retiming, Low Power Mode and Comodeling techniques for SoC verification.

### Senior R&D Engineer @ Mentor Graphics
Jan 2005 – Jan 2011 | Waltham, Massachusetts, United States
Developed highly optimized scalable bit-parallel cycle simulation "C" model that simulates the hardware acceleration behavior of FPGA chip.


## Education
### Bachelor's degree in Electrical and Electronics Engineering
Indian Institute of Technology, Kanpur

### Graduate Certificate in Data Science
Harvard Extension School


## Contact & Social
- LinkedIn: https://linkedin.com/in/shomitdutta
- GitHub: https://github.com/sdbma/

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Source: https://flows.cv/shomit
JSON Resume: https://flows.cv/shomit/resume.json
Last updated: 2026-04-12