Jeremy Aguilon
Algo Engineer at Hudson River Trading
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolJeremy Aguilon is an Algo Engineer at Hudson River Trading with a decade of software engineering experience building large-scale systems and machine learning infrastructure. A Georgia Tech CS alum, he previously led integrity-focused ML and extensibility work at Meta and has engineering stints across Google, Amazon, Lyft, and Amazon-facing personalization and cloud teams. He contributes to notable open-source robotics tooling—adding a Fixed-Lag Smoother implementation and tests to the widely used GTSAM library—bringing algorithmic rigor to production-grade C++ code. Comfortable across low-latency trading systems, backend services, and ML pipelines, he blends research experience from The Borg Lab with product-facing work in consumer and cloud platforms. Colocated in New York, he combines a hands-on coder’s craftsmanship with an emphasis on scalable, testable system design.
10 years of coding experience
5 years of employment as a software developer
Bachelor of Science (B.S.), Computer Science, Bachelor of Science (B.S.), Computer Science at Georgia Institute of Technology
Github Skills (10)
bayesian-network10
graph10
algorithms10
factors10
robotics10
c-language10
cprogramming-language10
python9
testing9
develop9
Programming languages (9)
Github contributions (5)
borglab/gtsam
Feb 2019 - Mar 2019
GTSAM is a library of C++ classes that implement smoothing and mapping (SAM) in robotics and vision, using factor graphs and Bayes networks as the underlying computing paradigm rather than sparse matrices.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:26 commits in 14 days
Contributions summary:Jeremy primarily contributed to the implementation of the Fixed-Lag Smoother within the gtsam_unstable library. This involved adding new classes, such as `FixedLagSmootherKeyTimestampMapValue`, `FixedLagSmootherResult`, and `BatchFixedLagSmoother`, alongside example implementations. The user also added unit tests to verify the functionality and correctness of the smoother implementation. This work demonstrates a focus on extending the library's capabilities with a specific smoothing algorithm.
smoothingsparsec-plus-plushierarchicalpattern-recognition
JerAguilon/CS3630-Perception-and-Robotics
Sep 2018 - Apr 2019
Contributions:13 commits, 1 push in 7 months
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