Member of Engineering - Reinforcement Learning
October 31
Description
• In this decade, the world will create artificial intelligence that reaches human level intelligence (and beyond) by combining learning and search. • Poolside exists to be one of these companies - to build a world where AI will drive the majority of economically valuable work and scientific progress. • You would be working on our reinforcement learning team focused on improving reasoning and coding abilities of Large Language Models through reinforcement learning. • This is a hands-on role where you'll work end-to-end from researching new exploration or training algorithms, to designing and scaling up RL environments, to implementing your ideas across the stack.
Requirements
• Experience with Large Language Models (LLM) • Deep knowledge of Transformers is a must • Strong deep learning fundamentals • Trained and fine-tuned LLMs from scratch • Extensively used and probed LLMs, familiarity of their capabilities and limitations • Knowledge/Experience of distributed training • Strong machine learning and engineering background • Research experience • Experience in proposing and evaluating novel research ideas • Familiar with, or contributed to the state of the art in at least one of the topics: LLMs, reinforcement learning, source code generation, continual learning • Is comfortable in a rapidly iterating environment • Is reasonably opinionated • Recent academic publications are nice to have • Programming experience • Linux • Strong algorithmic skills • Python with PyTorch or Jax • Use modern tools and are always looking to improve • Strong critical thinking and ability to question code quality policies when applicable • Prior experience in non-ML programming, especially not in Python - is a nice to have
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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