Founding Research Engineer at Flower

Do you want to push the boundaries of what frontier AI models can be? Join as one of the founding members of the Flower Frontier Model Team, a new group at Flower Labs charged with building category-defining models that blend the bleeding-edge in existing practices together with Flower’s pioneering decentralized learning methods. This is a fundamentally different direction than the one vanilla frontier labs are taking, one that not only eases the path to GPU scaling but also unlocks new data silos currently unable to be leveraged for frontier model training.

We will ship models with superhuman capabilities in domains spanning science, health, finance, drug discovery, and more. This is an opportunity to help invent and build the training paradigms that will define the next decade of AI, and to work on technologies that others will study, emulate, and build upon.

About the Role

(Preference given to candidates with post-training expertise. But any talented individual with a track record of exceptional drive and determination are encouraged to apply regardless of prior experience.)

As a founding Research Engineer in this new team, you will play a critical role in building SOTA LLMs and foundation models within a small, high-impact team composed of contributors that have a mix of both research and engineering backgrounds. This role will provide the opportunity to shape every part of the scientific foundation of our frontier models. You’ll be deeply hands-on, turning your best ideas into working systems and collaborating with the team to scale the approaches that prove most effective. The methods you develop will be used to produce world-leading models that are open-sourced and integrated into new Flower Lab products.

We expect you to bring to the team creativity, balanced against systematic experiments, and a keen awareness of the latest result from other AI labs. This will feed into how you design and implement techniques and run experiments across the spectrum of stages relevant to frontier model building: data curation, evals, pre-training, post-training. Everything is in scope for you as the team seeks to release its first series of models. Experience in these areas is obviously welcome, but a general expectation of problem solving, learning on the job and working collaboratively to efficiently combine the talents of the team is an explicit requirement for success. Familiarity in some mix of areas including the following will be necessary: transformers and their variants; optimization approaches — incl. theory and trade-offs; large-scale training stability; and finally, SFT, preference modeling, RL and evals.

This is a foundational role for an ambitious technical effort. We are looking for a special talent that brings to the team a strong principled scientific approach coupled with a talent for prototyping and all kinds of implementations. We seek out those able assume technical leadership as the ambitions of our models scale in complexity and capability. More broadly, you can expect a collaborative, fast-paced and demanding start-up environment containing a team of experts in their respective fields, in which everyone still learns something new every day. You will have the opportunity to contribute ideas, be heard and influence the direction of the company across the board.

About the Company

Flower Labs is the world-class AI startup best known for being behind the most popular open-source framework in the world for training AI on distributed data and compute resources using decentralized and federated methods. Trusted by industry leaders such as Mozilla, JP Morgan, Owkin, Banking Circle and Temenos use Flower to easily improve their AI models on sensitive data that is distributed across organizational silos or user devices. In a world where most AI relies on centralized public datasets — just a fraction of the data available — we believe unlocking access to (orders of magnitude more) sensitive data will drive the next breakthroughs in artificial intelligence.

Flower Labs is a Y Combinator (YCW23) graduate and backed by top-tier investors and renowned angels, including Felicis, First Spark Ventures, Mozilla Ventures, Hugging Face CEO Clem Delangue, GitHub Co-Founder Scott Chacon, Factorial Capital, Betaworks, and Pioneer Fund. Together, we are redefining how AI is built, deployed, and scaled.

Must Have Skills

• Deep understanding of recent architectures and training methodology used for LLMs and foundation models

• Experience with pre-training or post-training (SFT, RLHF, DPO, reward modeling, or equivalent) — note, preference will be given to individuals with post-training experience.

• Strong grounding in optimization techniques: AdamW variants, LR scheduling, mixed precision, stabilization methods, and scaling behaviors

• Strong experimental design skills: ablations, controlled comparisons, scaling experiments

• Fluency in PyTorch or JAX for implementing research ideas efficiently

• Ability to collaborate effectively with both research-oriented and engineering-oriented colleagues

• Ability to turn conceptual research directions into runnable prototypes that integrate into the training system

• Familiarity with common tools (Linux command line, git, Docker, …)

• Openness to adopting new tooling

• Strong written English

• Open, honest and transparent communication skills

Optional Skills

• PhD or Masters degree in a relevant discipline

• Knowledge in distributed training frameworks (FSDP, ZeRO, tensor/sequence/pipeline parallelism)

• Experience running large experiments on multi-GPU or multi-node clusters

• Experience in RLHF at scale, advanced preference modeling techniques, or safety alignment

• Prior experience in advanced distributed training frameworks and concepts

• Track record of working in open-source projects

• Strong publication record in ML, NLP, optimization, or related fields