Research Associate: Efficient, Adaptive LLM Architectures

Imperial College London; Greater London; On-site; GBP 51,000 - 59,000; Full time

Benefits offered by this job

Conference travel funding

Compute resources (GPUs)

World-leading institution

Generous pension

Job summary

Imperial College London, South Kensington campus, invites post-doctoral researchers to join the ERC AToM project on Adaptive Tokenization and Memory in Foundation Models. The role focuses on designing, implementing, and publicly releasing efficient LLM architectures with adaptive memory, latent tokenization, and sparse attention.

You will conduct original research in efficient adaptive LLMs, explore long-context reasoning for code, maths and agentic workflows, publish in top venues, and

Qualifications

• PhD in computer science or equivalent.
• Strong track record in top AI/ML/NLP conferences/journals.

Responsibilities

• Designing and developing novel AI architectures.
• Performing retrofitting, post-training, and evaluation of SOTA open-weight models.
• Publishing results in top-tier conferences and journals.
• Implementing research ideas using PyTorch/JAX, HuggingFace transformers/diffusers, and efficient kernels (Triton/CUDA).
• Contributing to research on adaptive memory, latent tokenization, sparse attention, and long-context understanding.
• Contributing to grant proposals and collaborative research initiatives.

Skills

Research excellence

Programming

LLM training

PEFT

CUDA kernels

Pytorch/JAX

PhD holder

Education

PhD in Computer Science

Tools

PyTorch

JAX

HuggingFace

Diffusers

CUDA

Triton

Job description

Imperial College London, South Kensington campus, invites post-doctoral researchers to join the ERC AToM project on Adaptive Tokenization and Memory in Foundation Models. The role focuses on designing, implementing, and publicly releasing efficient LLM architectures with adaptive memory, latent tokenization, and sparse attention.

You will conduct original research in efficient adaptive LLMs, explore long-context reasoning for code, maths and agentic workflows, publish in top venues, and