Robert Tinn
Applied Evals at OpenAI
Stony Stratford, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolRobert Tinn is an applied evaluations engineer and machine learning practitioner with nine years of industrial experience building practical AI solutions across Microsoft, Databricks, and OpenAI. He combines hands-on model development—evidenced by contributions to the well-known Infer.NET project, where he enhanced a Gaussian Process regressor tutorial with robust likelihoods and evaluation metrics—with customer-facing solutions architecture roles that bridge research and production. Comfortable moving between prototyping Bayesian models and architecting scalable ML deployments, he focuses on making principled methods operational and interpretable. Based in Stony Stratford and trained at Cambridge (MEng Information and Computer Engineering), he brings a mix of academic rigor and production-first pragmatism. An understated strength is his knack for improving model evaluation and visualization, turning subtle algorithmic tweaks into clearer, more actionable insights for teams and stakeholders.
9 years of coding experience
6 years of employment as a software developer
Master of Engineering - MEng, Information and Computer Engineering, Master of Engineering - MEng, Information and Computer Engineering at University of Cambridge
French, English
Github Skills (6)
machine-learning10
bayesian10
python10
bayesian-inference10
gaussian-processes10
data-analysis9
Programming languages (3)
Github contributions (5)
dotnet/infer
Aug 2019 - Sep 2019
Infer.NET is a framework for running Bayesian inference in graphical models
Role in this project:
Data Scientist
Data ScientistContributions:31 commits, 1 PR in 13 days
Contributions summary:Robert primarily contributed to a Gaussian Process regressor tutorial within the Infer.NET framework. They implemented and refined the tutorial code, adding features such as a Student-t likelihood option and RMSE calculation for model evaluation. Their commits included modifying the existing code to incorporate the new likelihood and plot the results and correcting the plotting results for better visualizations.
bayesian-inferencegraphical-modelsmachine-learning
r-tinn/infer
Aug 2019 - Sep 2019
Infer.NET is a framework for running Bayesian inference in graphical models
Contributions:11 PRs, 16 pushes, 5 branches in 14 days
bayesian-inferencegraphical-models
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