About the job
Are you eager to explore the frontiers of AI? Join us as a founding member of the Flower Frontier Model Team, an innovative group at Flower Labs. Our mission is to develop groundbreaking models that merge state-of-the-art practices with Flower's revolutionary decentralized learning techniques. This role takes a significantly different approach compared to traditional frontier labs, facilitating GPU scaling while unlocking new data silos for frontier model training.
Our goal is to create models with superhuman capabilities across various domains, including science, health, finance, drug discovery, and beyond. This is your chance to help innovate and establish the training paradigms that will shape the next decade of artificial intelligence and work on technologies that will influence future developments in the field.
Position Overview
Preference will be given to candidates with post-training expertise, but we encourage all passionate individuals with a proven record of excellence to apply, regardless of their prior experience.
As a founding Research Engineer, you will be instrumental in developing state-of-the-art LLMs and foundational models within a compact, high-impact team of professionals with diverse research and engineering backgrounds. You will have the opportunity to influence every aspect of the scientific groundwork for our frontier models. Expect to be hands-on, transforming your best ideas into functional systems and working closely with the team to scale the most effective strategies. The techniques you create will contribute to world-leading models that will be open-sourced and integrated into new Flower Lab products.
We anticipate that you will infuse creativity into your work while maintaining a methodical approach to experimentation and remain informed about the latest advancements from other AI laboratories. This knowledge will guide how you design and implement techniques and conduct experiments across all stages relevant to frontier model development: data curation, evaluations, pre-training, and post-training. Everything is on the table as we aim to release our first series of models. While experience in these areas is valued, a strong emphasis on problem-solving, on-the-job learning, and collaborative work to effectively leverage the team's diverse talents is essential for success.
