Ben Sandler is a Senior Software Engineer based in the New York City area with 11 years of experience building high-throughput, production systems for companies like Stripe and Opendoor. He specializes in back-end services, performance engineering, and data-driven pricing systems—at Opendoor he helped re-architect pricing APIs that improved accuracy by roughly $1,500 per home and ran real-time services making offers on over a million homes. His open-source contributions include performance and robustness improvements to go-torch, a stochastic flame graph profiler used for real-time sampling and visualization, reflecting deep systems and observability expertise. Ben combines Python, Go, and ML/Big Data tooling (Spark, Airflow, Pandas) with practical product sensibilities honed at Google and in nonprofit leadership at Hack4Impact. He graduated from the University of Pennsylvania with a 3.9 GPA and is known for bringing both measurable business impact and thoughtful engineering trade-offs to large-scale systems.
11 years of coding experience
7 years of employment as a software developer
High School, 4.0 / 4.0 (GPA), High School, 4.0 / 4.0 (GPA) at Cypress Bay High School
Bachelor’s Degree, Computer Science, 3.9 / 4.0 (GPA), Bachelor’s Degree, Computer Science, 3.9 / 4.0 (GPA) at University of Pennsylvania
A simple Flask boilerplate app with SQLAlchemy, Redis, User Authentication, and more.
Role in this project:
Back-end Developer
Contributions:114 commits, 59 PRs, 69 pushes in 2 years 5 months
Contributions summary:Ben contributed to the Flask-base repository by implementing features such as automatic gzip compression. They also made the project conform to PEP8 style guidelines. Furthermore, the user defined setup commands to prepare the database and create roles. The commits demonstrate experience with Flask, SQLAlchemy, and user management components.
Contributions:41 commits, 20 PRs, 26 pushes in 5 months
Contributions summary:Ben primarily focused on improving the performance and robustness of the Go-based profiling tool. Their work included adding cycle detection within the graph traversal algorithm to prevent infinite loops, a critical feature for flame graph generation. Further improvements involved enhancing the core functionality by fixing bugs, improving documentation, and optimizing code, such as refining the search logic for flame graph scripts, indicating their focus on enhancing the user experience and reliability of the tool.
golangstochasticprogramsflamegraph
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