Zach Olivier
Machine learning engineer. ML systems, evaluation, reliability, and applied research tooling.
I build production-shaped machine learning systems and research tools across data pipelines, model evaluation, post-training workflows, retrieval, anomaly detection, tabular modeling, and reliability analysis.
Experience
- Senior Machine Learning Engineer, SpaceX - production ML systems with high reliability requirements.
- Lead Data Scientist, DataStax - search, retrieval, telemetry, and applied ML for developer-facing products.
- Senior Machine Learning Engineer, Sony Pictures Entertainment - forecasting, experimentation, and media data products.
- Data Scientist, Keap - product analytics, lifecycle modeling, and growth measurement.
- Data Scientist / Analyst, Mazda North American Operations - modeling, analytics, and operational reporting.
Education
- M.S. Computer Science, Georgia Institute of Technology - machine learning, deep learning, reinforcement learning, probabilistic models, distributed systems.
- B.S. Statistics, San Diego State University - probability, mathematical statistics, linear algebra, regression, experimental design.
Technical Focus
- ML systems: training harnesses, inference workflows, telemetry, retrieval, and evaluation loops.
- Applied modeling: reliability, survival analysis, tabular ML, anomaly detection, and uncertainty.
- Research tooling: local paper pipelines, concept graphs, reproducible experiments, benchmark reports.
Selected Work
- Concensus SFT - scientific QA supervised fine-tuning pipeline.
- Second brain knowledge pipeline - local-first research and paper processing system.
- Industrial anomaly detection - PatchCore-style visual inspection lab.
- Tabular foundation model lab - TabPFN and TabICL baselines.
- Computational Life - artificial-life program-soup experiments.