Member of Engineering (Pre-training / Data Research) - 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.

About the Role

You’ll be working on our data team focused on the quality of the datasets being delivered for training our models. This is a hands-on role where your #1 mission would be to improve the quality of the pretraining datasets by leveraging your previous experience, intuition and training experiments. This includes synthetic data generation and data mix optimization. You’ll closely collaborate with other teams like Pretraining, Postraining, Evals, and Product to define high-quality data needs that map to missing model capabilities and downstream use cases.

Your Mission

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

Responsibilities

• Follow the latest research related to LLMs and data quality in particular. Be familiar with the most relevant open-source datasets and models.
• Design and implement complex pipelines that can generate large amounts of data while maintaining high diversity and optimizing the resources available.
• Closely work with other teams such as Pretraining, Posttraining, Evals and Product to ensure short feedback loops on the quality of the models delivered.
• Suggest, conduct and analyze data ablations or training experiments that aim to improve the quality of the datasets generated via quantitative insights.

Skills & Experience

• Strong machine learning and engineering background
• Experience with Large Language Models (LLM), including:
  • Understanding of transformer architectures and how LLMs learn
  • Data ablations and scaling laws
  • Mid-training and Post-training techniques
  • Training reasoning and agentic models
• Experience with evals tracking model capabilities (general knowledge, reasoning, math, coding, long-context, etc)
• Experience in building trillion-scale pretraining datasets, and familiarity with concepts like data curation, deduplication, data mixing, tokenization, curriculum, impact of data repetition, etc.
• Excellent programming skills in Python
• Strong prompt engineering skills
• Experience working with large-scale GPU clusters and distributed data pipelines
• Strong obsession with data quality
• Research experience:
  • Author of scientific papers on applied deep learning, LLMs, or source code generation is a nice to have
  • Can freely discuss the latest papers and descend to fine details
  • Is reasonably opinionated