AI Researcher — AI Architecture Research
At a glance
Mid-level AI Research role at Featherless AI. Remote · full-time.
Pay not stated AI Research salary
aimlroles summary, based on the employer's posting.
What you'll do
- Develop new model structures, including long-context, hybrid, and efficient sequence designs
- Test architecture changes for scale, efficiency, and stability using ablations, benchmarks, and empirical studies
- Write research papers for leading ML conferences and journals, alone or with co-authors
- Work with engineers to move research ideas into model training and inference systems
- Track emerging research and spot promising architecture directions early
What you bring
- A strong machine learning research background centered on model architecture
- Published work in ML or AI venues such as NeurIPS, ICML, or another listed venue
- Deep knowledge of neural networks, sequence models, attention, training dynamics, and optimization
- Practical experience using PyTorch or JAX
Who this fits
This suits a researcher who wants published architecture work to influence deployed models and can move between theory, experimentation, and engineering collaboration. The role is remote and emphasizes ownership of research direction.
From the employer
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 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
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