AI Researcher — AI Architecture Research

About the Role

We’re looking for anAI Researcher focused on AI architecture researchto 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 haspublished research papersand wants to see their work directly shape deployed models, not just benchmarks.

What You’ll Work On
* Research and designnovel AI architectures(e.g. alternatives to standard Transformer designs, long-context models, efficient sequence modeling, hybrid architectures)
Research and designnovel AI architectures(e.g. alternatives to standard Transformer designs, long-context models, efficient sequence modeling, hybrid architectures)
* Explore architectural improvements forscalability, efficiency, and stability
Explore architectural improvements forscalability, efficiency, and stability
* Prototype and evaluate new architectures through ablations, benchmarks, and empirical studies
Prototype and evaluate new architectures through ablations, benchmarks, and empirical studies
* Author and co-authorresearch papersfor top ML conferences and journals
Author and co-authorresearch papersfor top ML conferences and journals
* Collaborate with engineering teams to translate research into training and inference systems
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
Stay current with state-of-the-art research and identify promising directions early

What We’re Looking For
* Strong background inmachine learning research, with a focus on model architecture
Strong background inmachine learning research, with a focus on model architecture
* Publication recordin ML/AI venues (e.g. NeurIPS, ICML, ICLR, COLM, ACL, EMNLP, arXiv)
Publication recordin 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

Deep understanding of:
* Neural network architectures

Neural network architectures
* Sequence models and attention mechanisms

Sequence models and attention mechanisms
* Training dynamics and optimization

Training dynamics and optimization
* Hands-on experience with PyTorch or JAX

Hands-on experience with PyTorch or JAX
* Ability to reason rigorously, design clean experiments, and communicate results clearly
Ability to reason rigorously, design clean experiments, and communicate results clearly
* Comfortable working in a fast-moving startup environment
Comfortable working in a fast-moving startup environment

Nice to Have
* Experience withnon-Transformer architectures(e.g. RNN-based, state-space, hybrid models)
Experience withnon-Transformer architectures(e.g. RNN-based, state-space, hybrid models)
* Work on long-context or memory-efficient models
Work on long-context or memory-efficient models
* Open-source research contributions

Open-source research contributions
* Experience bridging research and production systems
Experience bridging research and production systems
* Background in efficient training or inference-aware architecture design
Background in efficient training or inference-aware architecture design

Why Join Us
* High ownership over research direction and roadmap
High ownership over research direction and roadmap
* Clear path topublishing impactful work
Clear path topublishing impactful work
* Tight feedback loop between research and real-world deployment
Tight feedback loop between research and real-world deployment
* Small, highly technical team with strong research culture
Small, highly technical team with strong research culture
* Competitive compensation and meaningful equity
Competitive compensation and meaningful equity

Originally posted onHimalayas