Member of Engineering (Post-training) at Poolside, a startup building AGI. Role focuses on post-training algorithms, data pipelines, and fine-tuning LLMs for agentic use cases with access to thousands of GPUs.
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.
We are a remote-first team that sits across Europe and North America and comes together once a month in-person for 3 days and for longer offsites twice a year.
Our R&D and production teams are 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.
You would be working as part of our Applied Research team, focused on turning pre-trained LLMs into well-aligned and highly capable AI systems for coding and software development. This is a hands-on role where you'll work across a variety of efforts, including: Building data pipelines and environments for agentic use cases, researching and implementing post-training algorithms, designing experiments and testing hypothesis, and more. You will have access to thousands of GPUs in this team.
To turn pre-trained LLMs into well-aligned and highly capable AI systems.
Research and experiment on ways to specialize foundational models to agentic use cases
Build and maintain data and training pipelines
Keep up with latest research, and be familiar with state of the art in LLMs, alignment, synthetic data generation, code generation
Design, analyze, and iterate on training/fine-tuning/data generation experiments
Write high-quality, pragmatic code
Work as part of a team: plan future steps, discuss, and communicate clearly with your peers
Experience with Large Language Models (LLM)
Deep knowledge of Transformers
Strong deep learning fundamentals
Good taste in data
Post-training experience with LLMs
Extensively used and probed LLMs, familiarity of their capabilities and limitations
Knowledge 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 multiple of the following topics: Fine-tuning and alignment of LLMs, synthetic data generation, continual learning, RLVR, code generation
Is comfortable in a rapidly iterating environment
Is reasonably opinionated
Programming experience
Linux
Strong algorithmic skills
Python with PyTorch or Jax
Use modern tools, including latest code agents 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
Intro call with one of our Founding Engineers
Technical Interview(s) with one of our Founding Engineers
Team fit call with the People team
Final interview with one of our Founding Engineers
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