
Founding Research Engineer – Flower Frontier Model Team
Flower Labs
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇬🇧 United Kingdom
Visit company websiteJob Level
Senior
Tech Stack
DockerLinuxPyTorch
About the role
- Push the boundaries of frontier AI models
- Build category-defining models with decentralized learning methods
- Shape the scientific foundation of frontier models
- Collaborate with a small, high-impact team composed of both research and engineering backgrounds
- Design and implement techniques across data curation, evals, pre-training, and post-training
- Release the first series of models that are world-leading and open-sourced
Requirements
- 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)
- 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
Benefits
- Open-source AI projects
- Work on AI that blends decentralized methods
- Collaborative startup environment
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
deep learninglarge language modelsfoundation modelspre-trainingpost-trainingoptimization techniquesexperimental designPyTorchJAX
Soft skills
collaborationcommunicationexperimental design skillsopenness to new toolingstrong written English