AI Researcher — AI Architecture Research

Sobre a oportunidade

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

Originally posted on Himalayas