Key skills
Machine learningLarge Language Models (LLM)Data ablationScaling lawsPost-training techniquesTraining reasoning modelsAgentic modelsSynthetic data generationData curationData deduplicationData mixingTokenizationCurriculum learningPython programmingPrompt engineeringGPU clustersDistributed data pipelinesScientific research in deep learningSource code generationPythonAIMachine LearningDeep LearningLLMLarge Language ModelsAgentic
About this role
Poolside is a company focused on building a world where AI drives economically valuable work and scientific progress. The Member of Engineering will work on the data team to improve the quality of pretraining datasets by generating synthetic data and collaborating with other teams to ensure high-quality data delivery.
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
Requirements:
- 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