Ben Firshman
San Francisco, California, United States
Summary
🤩
Rockstar🎓
Top SchoolBen Firshman is an experienced software engineer and entrepreneur with 18 years building developer tools, container infrastructure, and ML platforms from idea to production. Based in San Francisco, he founded Replicate and previously led product at Docker, where he helped create Docker Compose and other widely used tooling, and more recently served in senior AI platform roles. He combines deep backend and DevOps expertise (Docker, Machine, cog, docker-py) with full-stack work on projects like arXiv Vanity and a JS NES emulator, showing fluency across systems, APIs, and responsive frontends. Ben is a prolific open-source contributor who has implemented core clients, improved scraping/rendering pipelines, and optimized performance-critical code in multiple ecosystems. Comfortable as both founder and individual contributor, he accelerates projects by bridging product, engineering, and developer experience. He prefers email contact (ben@firshman.com) and maintains a public portfolio at firshman.com.
18 years of coding experience
13 years of employment as a software developer
BSc Computer Science, BSc Computer Science at University of Warwick
Stackoverflow
63reputation
1kreached
0answers
3questions
Github Skills (96)
apim10
package-management10
python10
testing10
command-line-interface10
databases10
erb10
mappings10
build-automation10
dockerfile10
650210
web-development10
emulation10
ruby-on-rails10
flask10
Programming languages (28)
Github contributions (5)
arxiv-vanity/arxiv-vanity
Aug 2017 - Jan 2022
Renders papers from arXiv as responsive web pages so you don't have to squint at a PDF.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:455 commits, 202 PRs, 401 pushes in 4 years 6 months
Contributions summary:Ben contributed significantly to the backend functionality of the arXiv Vanity project, focusing on paper scraping and data processing. They implemented a system to download papers from the Arxiv API, parse the results, and insert new papers into a Django database. The contributions also include unit tests for the scraping functionality, and setting up a process to render the papers. This involved modifying project settings, creating database models, and defining management commands for scraping and rendering.
academic-publishingarxivsciencepdflatex
replicate/cog
Mar 2021 - Jan 2023
Containers for machine learning
Role in this project:Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:120 reviews, 331 commits, 335 PRs in 1 year 10 months
Contributions summary:Ben primarily contributed to the project by modifying Docker-related commands and configurations to improve build and execution environments. They added environment variables to Docker commands to ensure proper functionality on different machines. Their work extended to refactoring and modifying the project's server-side codebase, including modifications to configuration files and the implementation of external libraries for tasks such as zip archive handling. The user also introduced improvements to the build process, including the addition of a flag for GPU support.
containerscudapytorchdeep-learningdocker
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