Member of Engineering (Pre-training / Synthetic Data)

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.

ABOUT OUR TEAM

We are a remote-first team that sits across Europe and North America. We come together once a month in-person 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 combination of more research and more engineering-oriented profiles, however, everyone deeply cares about the quality of the systems we build and has a strong underlying knowledge of software development. We believe that good engineering leads to faster development iterations, which allows us to compound our efforts.

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 role particularly focuses on generating synthetic data at scale and determining the best strategies to leverage such data into training large models. 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.

Staying in sync with the latest research in synthetic data generation and pretraining is key to success in this role. You will constantly lead original research initiatives through short, time-bounded experiments while deploying highly technical engineering solutions into production. With the volumes of data to process being massive, you'll have a performant distributed data pipeline together with a large GPU cluster at your disposal.

YOUR MISSION

To deliver large, high-quality, and diverse synthetic datasets mixing natural language and code modalities to train best-in-class coding agents.

RESPONSIBILITIES

• Follow the latest research related to LLMs and synthetic data generation 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 alignment on the quality of the models delivered.

• Continuously measure and refine the quality of the datasets being generated while validating the final data strategy through quantitative data ablation experiments.

SKILLS & EXPERIENCE

• Strong machine learning and engineering background

• Experience with Large Language Models (LLM), including:

  • Understanding of how LLMs learn

  • Data ablations and scaling laws

  • Post-training techniques

  • Training reasoning and agentic models

• Experience with implementing cost-efficient, complex pipelines to generate synthetical datasets at scale optimizing for data quality, correctness, diversity, etc.

• 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 any of the topics: applied deep learning, LLMs, source code generation, etc. - is a nice to have

  • Can freely discuss the latest papers and descend to fine details

  • Is reasonably opinionated

PROCESS

• Intro call with one of our Founding Engineers

• Technical Interview(s) with one of our Members of Engineering

• 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 and dependents

• Company-provided equipment

• Wellbeing, always-be-learning and home office allowances

• Frequent team get togethers

• Great diverse & inclusive people-first culture

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