AI Researcher — AI Architecture Research
Featherless AI · Remote (world)
Extracted from the posting
Researches and prototypes new model architectures, including Transformer alternatives, long-context models and hybrid designs, through ablations and benchmarks. Works with engineering teams to translate architecture research into training and inference systems. Stack is PyTorch or JAX.
About the Role
We’re looking for an AI Researcher focused on AI architecture research to help design, analyze, and advance next-generation model architectures. You’ll work at the intersection of theory and production—publishing novel research while collaborating closely with engineers to turn ideas into real systems.
This role is ideal for someone who has published research papers and wants to see their work directly shape deployed models, not just benchmarks.
What You’ll Work On
- Research and design novel AI architectures (e.g. alternatives to standard Transformer designs, long-context models, efficient sequence modeling, hybrid architectures)
- Explore architectural improvements for scalability, efficiency, and stability
- Prototype and evaluate new architectures through ablations, benchmarks, and empirical studies
- Author and co-author research papers for top ML conferences and journals
- Collaborate with engineering teams to translate research into training and inference systems
- Stay current with state-of-the-art research and identify promising directions early
What We’re Looking For
- Strong background in machine learning research, with a focus on model architecture
- Publication record in ML/AI venues (e.g. NeurIPS, ICML, ICLR, COLM, ACL, EMNLP, arXiv)
- Deep understanding of:Neural network architecturesSequence models and attention mechanismsTraining dynamics and optimization
- Hands-on experience with PyTorch or JAX
- Ability to reason rigorously, design clean experiments, and communicate results clearly
- Comfortable working in a fast-moving startup environment
Nice to Have
- Experience with non-Transformer architectures (e.g. RNN-based, state-space, hybrid models)
- Work on long-context or memory-efficient models
- Open-source research contributions
- Experience bridging research and production systems
- Background in efficient training or inference-aware architecture design
Why Join Us
- High ownership over research direction and roadmap
- Clear path to publishing impactful work
- Tight feedback loop between research and real-world deployment
- Small, highly technical team with strong research culture
- Competitive compensation and meaningful equity
