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

About Poolside

Poolside exists to build a world where AI will be the engine behind economically valuable work and scientific progress. We believe the fastest way to reach AGI lies in accelerating software development itself, by reshaping the developer experience with agentic systems, coding assistants, and the frontier models that power them. We deploy these systems directly into the development environments of security-conscious enterprises.

About Our Team

We were founded in the US and have our home there, but our team is distributed across Europe and North America. We get our fix of in-person collaboration (and croissants) in Paris each month for 3 days, always Monday-Wednesday, with an open invitation to stay the whole week. We also do longer off-sites once a year.

Our team is a multidisciplinary blend of research, engineering, and business experts. What unites us is our deep care for what we build together.

About the Role

You will be a core member of our Pretraining Data team, responsible for building and scaling our Model Factory: our system for quickly training, scaling, and experimenting with our foundation models. This is a hands-on role where your #1 mission is to architect and maintain the high-performance pipelines that transform trillions of raw tokens into the high-quality dataset "fuel" our models require.

To enable us to conduct and implement latest research, you’ll be engineering the ingestion, deduplication, and streaming systems that handle petabyte-scale data. You will bridge the gap between raw web crawls and our GPU clusters, directly influencing model performance through superior data modeling, algorithmic sorting, and distributed pipeline optimization.

Your Mission

To deliver large, high-quality, and diverse datasets of natural language and source code for training Poolside models and coding agents.

Responsibilities

• Build and maintain high-performance pipelines for trillions of tokens.
• Deliver diverse and high quality datasets for pre-training foundation models.
• Closely work with other teams such as Pretraining, Posttraining, Evals and Product to ensure alignment on the quality of the models delivered.

Skills & Experience

• 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: e.g., 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
• Nice to have:
  • Experience in 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

Benefits

• 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 & home office allowances
• Frequent team get-togethers
• Diverse & inclusive people-first culture