Andrew Gross is a Staff AI Engineer based in New York with 14 years of experience and a decade-plus tenure at YipitData where he progressed from senior developer to Director of Engineering and now leads AI efforts. He designs and ships data-driven systems that analyze billions of signals for institutional investors, balancing strategic leadership with hands-on engineering. His technical strengths span backend architectures, AWS infrastructure and production ML, and he’s contributed to the well-known moto project by improving EC2/autoscaling block device mapping and launch configuration tests. A Cornell-trained engineering physicist, he brings rigorous problem-solving and systems thinking to complex, high-throughput telemetry pipelines. Notably, he pairs org-level strategy with meticulous code-level work—fixing merge issues and extending test coverage in open-source tooling.
14 years of coding experience
15 years of employment as a software developer
Bachelor’s Degree, Engineering Physics/Applied Physics, Bachelor’s Degree, Engineering Physics/Applied Physics at Cornell University
A library that allows you to easily mock out tests based on AWS infrastructure.
Role in this project:
Back-end Developer
Contributions:6 commits, 2 PRs, 1 branch in 2 years 7 months
Contributions summary:Andrew primarily focused on implementing and modifying features related to block device mapping within the `moto` project, which simulates AWS infrastructure. Their contributions involved changes to the `autoscaling` and `ec2` modules, adding support for block device configurations. They also enhanced the tests to cover the new functionality, along with fixing merge issues. These changes involved adding features to the launch configuration backend.
Contributions:94 commits, 9 PRs, 82 pushes in 5 years 5 months
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