Member of Engineering – Pre-training, Synthetic Data

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poolside

Site WebLinkedInTous les Emplois

51 - 200 employés

Fondée en 2023

🤖 Intelligence artificielle

🏢 Entreprise

Artificial Intelligence • Enterprise

Poolside est un accélérateur spécifiquement conçu pour les fondateurs et builders Web3. Il apporte un soutien aux projets en finance décentralisée (DeFi), gaming, gouvernance, infrastructure et NFT. Fort d’un écosystème de 20 000 membres, incluant des mentors, des investisseurs et des builders Web3, Poolside a co-lancé et accompagné plus de 110 projets. L’accélérateur offre un accès privilégié au mentorat et à l’expertise technique pour aider les projets Web3 à scaler et à réussir leurs lancements. Poolside collabore également avec des entreprises et des protocoles de premier plan pour stimuler la croissance et l’innovation dans l’écosystème Web3.

Description

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

🎯 Exigences

• Strong machine learning and engineering background • Experience with Large Language Models (LLM) • 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

🏖️ Avantages

• 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