Member of Engineering (Pre-training / Data Engineering) at Poolside building high-performance data pipelines for foundation model training. Responsible for ingestion, deduplication, and streaming systems handling petabyte-scale data to fuel model development.
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.
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.
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 stack in this blogpost from our 'Model Factory' series.
To deliver large, high-quality, and diverse datasets of natural language and source code for training Poolside models and coding agents.
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.
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
Intro call with Eiso, our CTO & Co-Founder
Technical Interview(s) with one of our Founding Engineers
Team fit call with the People team
Final interview with one of our Founding Engineers
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