Frank Lanasa
Software Technical Lead at WHOOP
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolFrank Lanasa is a Senior Software Engineer in Boston with nine years building cloud-native, full‑stack and ML tooling across startups and enterprise teams. He’s shipped production systems from APIs and role-based access with FastAPI to scalable AutoML pipelines and CI/CD for Alteryx AI Studio, and has hands-on experience with Python, Scala, Elixir, React/TypeScript, Docker and Kubernetes. As an early engineering hire at SurfaceAI and a lead at Alteryx, he’s combined feature delivery with infrastructure and performance work—cutting model generation times and enabling LLM fine-tuning on private data. He contributes to open-source MLOps projects like EvalML, improving CI, documentation, and performance testing, showing attention to developer experience as well as ML reliability. Trained in sociology and advanced education at Harvard, he brings a data-informed, user-centered perspective to engineering problems and a track record of translating research-style rigor into production-ready systems.
9 years of coding experience
8 years of employment as a software developer
Bachelor of Arts (B.A.) Sociology, Bachelor of Arts (B.A.) Sociology at Harvard UniversityWeb Development, Web Development at Launch AcademyDoctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at Harvard Graduate School of Education
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (9)
github-ci10
githubaction-workflow10
devops10
cicd10
documentation9
testing9
python8
automl8
machine-learning7
Programming languages (8)
Github contributions (5)
alteryx/evalml
Jan 2022 - Sep 2022
EvalML is an AutoML library written in python.
Role in this project:
MLOps Engineer
MLOps EngineerContributions:45 reviews, 39 commits, 25 PRs in 8 months
Contributions summary:Frank primarily contributed to the project by enhancing the CI/CD pipeline, improving documentation, and making various quality-of-life improvements. They updated the documentation to automate dead link detection and exclude non-latest versions. Additionally, they added `vowpalwabbit` to the local recipe and fixed the performance test job. They also added a workflow to run looking glass performance tests.
pythondata-sciencemodel-selectionoptimizationmachine-learning
ETMAInc/TruffleHunt-ReactComponents
Mar 2018 - Sep 2018
Common React components used across our suite
Contributions:25 PRs, 120 pushes, 17 branches in 5 months
reactreact-componentsreactjstypescriptsuite
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.