Poolside

About Poolside 
 
In this decade, the world will create Artificial General Intelligence. There will only be a small number of companies who will achieve this. Their ability to stack advantages and pull ahead will define the winners. These companies will move faster than anyone else. They will attract the world's most capable talent. They will be on the forefront of applied research, engineering, infrastructure and deployment at scale. They will continue to scale their training to larger & more capable models. They will be given the right to raise large amounts of capital along their journey to enable this. They will create powerful economic engines. They will obsess over the success of their users and customers. 
 
Poolside exists to be this company: 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. We’re in a race that requires hard work, intellectual curiosity, and obsession; to balance this intensity, we’ve assembled a team of low ego and kind-hearted individuals who have built the special culture Poolside has. By building collaboratively and with intention, we create a compounding effect that moves the entire company forward towards our mission: reaching AGI through intelligence systems built for software development. 
 
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. You will be closely collaborating with other teams like Pretraining, Postraining, Evals, and Product to generate high-quality datasets that map to missing model capabilities and downstream use cases. 
 
Curious about the tech? Take a deep dive into our pretraining data work in our Laguna M.1/XS.2 Technical Report. 
 
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 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 (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
* 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 
 

PROCESS 
 


* Intro call with one of our Founding Engineers
* Technical Interview(s) with one of our Founding Engineers
* Team fit call with the People team
* Final interview with one of our Founding Engineers 
 
 

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

Data

Software Development

Research

Machine Learning

Data modeling

Infrastructure

Docker

Python

Continuous Integration

Cloud

Web Development

Team collaboration

Data Engineering

Airflow

Dagger

CI/CD

Grafana

Prometheus

Kubernetes

Cloud Services

Dask

Apache Spark

Member of Engineering (Pre-training / Data Engineering)