Featherlessai
AI Researcher — AI Architecture Research
5mo ago
pytorchjax
Research and design novel AI architectures and collaborate with engineering teams to implement them.
Responsibilities
- 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.
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
Conditions
- 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
Other
- 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
- 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 architectures
- Sequence models and attention mechanisms
- Training 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