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Research Scientist – LLM Architectures
CommonAI CIC. Design and implement new model components, including attention variants, structured or sparse layers, routing mechanisms, activations and training objectives .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing advanced model components, particularly in large language models, while ensuring rigorous experimental design and effective collaboration with engineering teams. Proficient in leveraging tools like PyTorch and JAX for efficient model training and analysis.
Highest-signal resume keywords
PhD In Machine LearningHands-On Experience Pretraining Transformer Language ModelsStrong PyTorch ExperienceKnowledge Of LLM Inference CostsExperience Running Workloads On HPC Systems
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Attention MechanismsActivation FunctionsNormalisationMixture-Of-Experts RoutingSparse LayersExperimental DesignStatistical ComparisonMechanistic InterpretabilityGPU Kernel WorkQuantitative Analysis
Soft Skills
Strong Enthusiasm For ResearchCollaboration
Tools & Technologies
PyTorchJAXTritonCUDAHPC SystemsBatch SchedulerSlurm
Industry Keywords
Large Language ModelsAI Coding AssistantsAutonomous AgentsHardware-Model Co-DesignMemory HierarchiesInterconnects
Tech Stack
Tools & technologiesPyTorch
About the role
Key responsibilities & impact- Design and implement new model components, including attention variants, structured or sparse layers, routing mechanisms, activations and training objectives
- Run controlled pretraining experiments at matched compute, from small scale up to larger models
- Analyse structures learned by trained models and their implications for efficiency and interpretability
- Explore adapting existing open models through continued training or distillation
- Collaborate with research software and kernel engineers to turn ideas into reliable, reproducible training runs and efficient kernels
- Write papers, release code and present results
- Conduct research on large language model architectures that reduce inference costs while retaining performance
- Use LLMs, AI coding assistants and autonomous agents for research and software development
Requirements
What you’ll need- A PhD in machine learning, computer science, physics or a related field, or equivalent research experience
- Published or shipped research on new model components, such as attention mechanisms, activation functions, normalisation, mixture-of-experts routing, or structured or sparse layers
- Hands-on experience pretraining transformer language models from scratch, including careful ablations at matched compute
- Strong PyTorch or deep JAX experience and willingness to work in PyTorch, including multi-GPU training
- A working understanding of LLM inference costs, including KV cache, memory bandwidth and why decoding is memory-bound
- Rigour in experimental design, including strong baselines, statistical comparison and honest reporting of negative results
- Strong enthusiasm for using LLMs, coding assistants and agents as core tools in research and development
- GPU kernel work, such as Triton or CUDA, valued
- Experience with sparsity, pruning, mixture-of-experts or distillation of LLMs valued
- Knowledge of accelerator and cluster hardware, including memory hierarchies and interconnects, valued
- Experience running workloads on HPC systems with a batch scheduler such as Slurm valued
- Mechanistic interpretability experience valued
- Quantitative background from another field, such as physics, applied maths or neuroscience, valued
- Interest in hardware–model co-design valued
Benefits
Comp & perks- A collaborative and supportive work environment
- The opportunity to have a high impact in a growing organisation
- Competitive salary package and pension
- Professional development opportunities
- Networking opportunities with influential people from across the tech sector, financial services, and academia
- A vibrant office environment located a few minutes' walk away from Cambridge train station
- Equal opportunity and commitment to an inclusive and diverse workplace
- Top-tier Claude Code subscription