Benjamin Weitz Email & Phone Number

Oakland, Ca, Us

Oakland, Ca, Us

Bellevue, Washington, Us

Bellevue, Washington, Us

San Francisco, California, Us

I worked for Ads Relevance as a full-stack Machine Learning engineer. I trained multiple ML models end-to-end, ran A/B experiments to evaluate the impact of improved relevance predictions, and did feature selection and model parameter hypertuning while evaluating model performance. I was the DRI for engineering health on my team. I organized fixathons, handled cross-org requests for our team, organized and tracked our S3 data footprint and health, and developed new-hire onboarding documentation for our team. I was the engineer POC for Relevance in our org.

Berkeley, Ca, Us

I have taught CS170 Efficient Algorithms and Intractable Problems twice. The class covers introductory algorithms materials like recursion, dynamic programming, and greedy algorithms, as well as a very brief intro to complexity theory in reductions and the definition of NP.

Berkeley, Ca, Us

My responsibilities include doing independent research, helping to teach classes, and writing and publishing my work.

San Francisco, California, Us

I worked as an intern for Pinterest's Data Analytics team. Our team was responsible for providing the infrastructure and tools that the other teams would use to run experiments and analyze data. My main intern project was integrating XGBoost, a popular third-party package for running a Gradient Boosted Decision Trees algorithm, into our existing pipeline for distributed machine learning algorithms. I also evaluated it against our current implementation which was using Spark's built-in methods and determined that XGBoost offered significant scalability improvements. After integrating XGBoost, we could train a GBDT model on more than 7 times more data.

Pasadena, Ca, Us

I taught two classes twice at Caltech:CS21 Decidability and Tractability: Topics include complexity basics like automata, reductions, P vs. NP.CS38 Intro to Algorithms: Taught common algorithmic techniques and data structures like recursion, trees, dynamic programming, linear programming, greedy algorithms.

Doctor Of Philosophy (Ph.D.), Computer Science

B.S, Mathematics And Computer Science