Emily Dinan is a research engineer with eight years of experience building and hardening AI systems, currently at Meta after recent research roles at DeepMind and Facebook AI. She combines a strong mathematical background (MS and BS in Mathematics) with hands-on engineering, focusing on quality assurance and test automation for dialogue model frameworks like ParlAI. Her open-source contributions include adding and refining unit tests for core ParlAI examples, improving reliability for a widely used FacebookResearch toolkit. Emily is based in New York and brings a practical research-to-production mindset, bridging model development with rigorous validation. Colleagues rely on her to catch edge cases early—she has a knack for turning subtle evaluation gaps into concrete test coverage.
8 years of coding experience
6 years of employment as a software developer
Master of Science - MS, Mathematics, Master of Science - MS, Mathematics at University of Washington
Bachelor of Science - BS, Mathematics, Bachelor of Science - BS, Mathematics at Fordham University
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Role in this project:
QA Engineer / Test Automation Engineer
Contributions:76 reviews, 728 commits, 583 PRs in 4 years 4 months
Contributions summary:Emily's commits focused on adding and modifying unit tests for core Python examples within the ParlAI framework. They added tests for the `eval_model.py` and `display_data.py` examples and modified the `repeat_label` agent to check for `eval_labels`, indicating a focus on improving code quality and ensuring model functionality through testing. This suggests a role focused on quality assurance and automated testing.
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