Poolside - Member of Engineering (Pre-training / Data Engineering)

• Strong background in building production-grade, distributed data systems for machine learning, with experience in: • Orchestration: Slurm, Airflow, or Dagster • Observability & Reliability: CI/CD, Grafana, Prometheus, etc. • Infra: Git, Docker, k8s, cloud managed services • Batch inference (ex: vLLM) • Performance obsession, especially with large-scale GPU clusters and distributed pipelines • Expert-level python knowledge and ability to write clean and maintainable code • Strong algorithmic foundations • Proficiency with libraries like Polars, Dask, or PySpark • Experience in building trillion-scale SOTA pretraining datasets • Experience translating research to production at scale • Experience with OCR, web crawling, or evals • PROCESS • PROCESS • Technical Interview(s) with one of our Founding Engineers • Team fit call with the People team • Final interview with one of our Founding Engineers