Dipayan Mukherjee

Software Engineer

Kirkland, Washington, United States3 yrs experience

Key Highlights

Expert in developing live trading strategies.
Proficient in software engineering and quantitative finance.
Strong background in energy-efficient software solutions.

Stackforce AI infers this person is a Fintech and Software Engineering expert with a focus on quantitative analysis and algorithm development.

Contact

Skills

Core Skills

SoftwareQuantitative Finance

Other Skills

TPUsDVFSAndroid kernelEnergy efficiencyTrading strategiesData analysisAlpha developmentInSARCUDAPattern recognitionRecommendation systemsC++ProgrammingAlgorithmsData Structures

Experience

3 yrs

Total Experience

1 yr 1 mo

Average Tenure

9 mos

Current Experience

Google

Engineer

Sep 2025 – Present · 9 mos · Seattle, Washington, United States · On-site

I work on TPUs

TPUsSoftware

Google

Google Silicon Intern

May 2021 – Aug 2021 · 3 mos · Mountain View, California, United States

Categorized DVFS transition state graph, based on latency, energy and thermal constraints.
Modified the Android kernel to handle DVFS requests from multiple IPs.
Designed energy efficient strategies for handling DVFS requests.

DVFSAndroid kernelEnergy efficiencySoftware

Tower research capital

Quantitative Trading Intern

May 2019 – Aug 2019 · 3 mos · Manhattan, New York, United States

Developed live trading strategies in US.
Designed strategy combining fundamental values from various datasets
Created various grouping mechanisms for stocks

Trading strategiesData analysisQuantitative Finance

Worldquant llc

Quantitative Researcher

Jul 2016 – Jul 2018 · 2 yrs · Mumbai, Maharashtra, India

Developed over 500 live alphas on US, EUR and Asian market.
Focused on medium to low turnover alphas (1% to 15%).
Achieved an after cost Sharpe ratio of 3.00 (US) on simple combo logic.
Worked on dataset from various sources ranging from price-volume, fundamental, news, options, etc.

Alpha developmentData analysisQuantitative Finance

The university of british columbia

Research Intern

May 2015 – Jul 2015 · 2 mos · Vancouver, British Columbia ,Canada

Used persistent scatterer pixel selection for InSAR images (Satellite Images).
Parallelized the data processing in CUDA.
Modified Data Access Pattern to reduce the run time by half.

InSARCUDASoftware

Adobe

Research Intern

May 2014 – Jul 2014 · 2 mos · Bengaluru, Karnataka, India

We created customer profile based on their shopping pattern across various channels (online, retail).
We used pattern recognition to study customer’s behavior across various channels.
Designed a recommendation system for customers based on their cross channel preferences.
Designed an app for providing recommendation using customer’s location, which helps us in recommending products based on the items in his vicinity.

Pattern recognitionRecommendation systemsSoftware