Agam Gupta
Software Engineer II at Meta
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolAgam Gupta is a Software Engineer II at Meta with a decade of hands-on experience building scalable systems and research-driven tooling across industry and academia. A Berkeley EECS and Data Science graduate, he blends production engineering at Meta and internships at AWS and Atlassian with research work on LLM alignment and DSGE econometric modeling. His open-source contributions to FRBNY-DSGE/DSGE.jl show a rare mix of numerical optimization and software performance tuning—optimizing simulated annealing routines and impulse response analysis for macroeconomic models. At Berkeley he taught algorithms (CS 170), led consulting initiatives, and contributed hardware and firmware to the CalSol solar vehicle, demonstrating both software depth and cross-domain engineering fluency. Colleagues describe him as an engineer who moves comfortably between research prototypes and production-quality code, with an economist’s attention to model behavior under the hood. Based in San Francisco, he brings a pragmatic, data-informed approach to building reliable systems informed by academic rigor.
10 years of coding experience
5 years of employment as a software developer
Bachelor of Science - BS, EECS and Data Science, Bachelor of Science - BS, EECS and Data Science at UC Berkeley College of EngineeringArdrey Kell High School
English, Hindi, Spanish
Github Skills (7)
algorithm10
optimisation10
optimizers10
bayesian-inference10
optimization10
economics9
statistical-models8
Programming languages (2)
Github contributions (5)
FRBNY-DSGE/DSGE.jl
Nov 2016 - May 2018
Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 233 commits, 1 PR in 1 year 6 months
Contributions summary:Agam's contributions primarily focused on improving the performance and functionality of the Dynamic Stochastic General Equilibrium (DSGE) model. This included optimizing the `estimate/optimize.jl` and `src/estimate/simulated_annealing.jl` files for speed improvements, as well as adding and refining impulse response functions and their related analysis. The user also worked on features such as creating and saving means and bands for impulse responses and enabling the use of different temperature schedules in the simulated annealing process, showing a strong focus on model analysis and optimization.
solveequilibriumdynamical-systemsbayesian-inferencedsge
abhig94/dotfiles
Jul 2016 - Jun 2019
Contributions:7 pushes, 1 branch in 2 years 11 months
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