Member of Engineering (Pre-training / Data Engineering)

Stack

Responsibilities

• ▹Build and maintain high-performance pipelines for trillions of tokens
• ▹Deliver diverse, high-quality datasets for pre-training foundation models
• ▹Work closely with other teams (Pretraining, Posttraining, Evals, Product) to ensure model quality
• ▹Engineer ingestion, deduplication, and streaming systems at petabyte scale
• ▹Influence model performance through superior data modeling, algorithmic sorting, and distributed pipeline optimization

Requirements

• ▹Strong background in building production-grade, distributed ML data systems
• ▹Experience with orchestration: Slurm, Airflow, or Dagster
• ▹Observability and reliability tooling: CI/CD, Grafana, Prometheus
• ▹Infra experience: Git, Docker, Kubernetes, cloud managed services
• ▹Batch inference experience (e.g. vLLM)
• ▹Performance obsession, especially with large-scale GPU clusters and distributed pipelines
• ▹Expert-level Python knowledge, writing clean and maintainable code
• ▹Strong algorithmic foundations
• ▹Proficiency with Polars, Dask, or PySpark

Nice to have

• ▹Experience building trillion-scale SOTA pretraining datasets
• ▹Experience translating research to production at scale
• ▹Experience with OCR, web crawling, or evals
• ▹Prior experience pre-training LLMs

Soft skills

What we offer

• ▹Fully remote work & flexible hours
• ▹37 days/year of vacation & holidays
• ▹Health insurance allowance for you & dependents
• ▹Company-provided equipment
• ▹Well-being, always-be-learning, and home office allowances
• ▹Frequent team get-togethers
• ▹Diverse & inclusive people-first culture

About the company

Poolside exists to reach AGI by accelerating software development through agentic systems, coding assistants, and the frontier models that power them; its founding team is distributed across the US and Europe, meeting monthly in Paris.