AI Researcher — AI Architecture Research

RemoteSeniorResearch Scientistposted 9mo ago

✦Role summaryAI-generated

This role demands a researcher who bridges theoretical innovation in AI architectures with practical deployment, emphasizing novel designs beyond standard Transformer paradigms and measurable impact on real-world systems. The ideal candidate thrives at the intersection of academic rigor and engineering collaboration, ensuring research directly translates into scalable, efficient, and stable deployed models.

Skills required

Transformer architecture alternatives Long context modeling Efficient sequence modeling Model benchmarking Empirical research methods Scalability optimization Stability analysis Research publishing Prototype development Cross disciplinary collaboration AI Machine learning Neural networks PyTorch JAX

About this role

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