Research Associate in Adaptive and Efficient LLM Architectures

Within the project, you will conduct original research in the new and exciting field of efficient and adaptive LLM architectures and explore its applications across long-context understanding and reasoning (for code, maths, and agentic workflows) as well as long-horizon multimodal world modelling. We will strive to release new, more efficient and capable AI models and to publish in top-tier conferences and journals. Specifically, you will work closely with Dr. Edoardo Ponti in:

Designing and developing novel architectures for AI models

Performing retrofitting, post-training, and evaluation of SOTA open-weight models

Conducting independent and collaborative research within the group

Publishing results in top-tier conferences and journals (e.g., NeurIPS, ICML, ICLR, *ACL, EMNLP, Nature)

Implementing research ideas using modern deep learning frameworks (PyTorch/JAX), model/dataset libraries (Huggingface transformers/diffusers), and efficient kernels (Triton/CUDA)

Contributing to research projects on adaptive memory, latent tokenization, sparse attention, long-context understanding and reasoning, agentic, and multimodal world modelling

Contributing to the life and development of Edoardo Ponti’s Lab, including weekly meetings, presentations, maintaining the website and other resources).

Delivering tutorials at conferences and summer schools.

Mentoring PhD and MSc students.

Presenting research at international conferences and workshops.

Contributing to grant proposals and collaborative research initiatives.

Self-driven and motivated individuals with genuine love for research.

The applicant is also expected to have a strong track record in top conferences and journals in the fields of AI/ML/NLP, such as NeurIPS, ICML, ICLR, *ACL, EMNLP, etc. Candidates with research experience as part of frontier AI labs are also welcome.

Excellent skills in coding, strong foundations in mathematics (especially information theory, linear algebra, calculus), and knowledge in deep learning.

Practical experience in a broad range of techniques including LLM training, evaluation, RLVR, PEFT, quantisation, tensor/data parallelism.

Ideally, the candidate should have familiarity with CUDA kernels and/or Triton, and inference engines (vLLM, SGLang, et cetera).

Experience coding with deep learning libraries such as Pytorch/JAX is essential.

Fluent written and spoken English skills as well as contributions to the group culture are expected.

Applicants must hold a PhD in computer science or equivalent experience.

Please see job description for a full list of requirements.

Extensive funding for conference travel (2 international conferences per year)

Access to extensive compute via the AToM project GPUs (B200s and cloud credits) and GPUs from the Department of Computing and the College of Engineering (A100s and H200s)

The opportunity to continue your career at a world-leading institution and be part of our mission to continue science for humanity.

Grow your career: gain access to Imperial’s sector-leading dedicated career support for researchers as well as opportunities for promotion and progression.

Sector-leading salary and remuneration package (including 43 days off a year and generous pension schemes).

Be part of a diverse, inclusive and collaborative work culture with various staff networks and resources to support your personal and professional wellbeing.

Full-time, fixed term 2-year contract to start around winter 26/27 (the start date is flexible).

*Candidates who have not yet been officially awarded their PhD will be appointed as Research Assistant within the salary range £45,399 - £48,876 per annum.

In addition to completing the online application candidates should attach:

A full CV with a list of all publications

A research statement (max 2 pages) i ndicating what you see are the most interesting research questions relating to adaptive and efficient AI architectures and why your expertise is relevant.

Informal enquiries related to the position should be directed to Dr Edoardo Ponti:

eponti@ic.ac.uk

For queries regarding the application process contact Jamie Perrins:

j.perrins@imperial.ac.uk