Research Scientist (Applied LLMs), London

Research Scientist - Applied Large Language Models

Your impact

As a Research Scientist focused on the application of Large Language Models (LLMs), you will play an exciting role in bringing cutting edge technologies to bear across our research areas to transform the drug discovery world as we know it.

Working in a highly creative, fast-paced and interdisciplinary environment, you will be partnering with leading research scientists and engineers to conceive, design, and develop cutting edge LLM applications that help to unlock new modelling capabilities and predictive power which will be critical to the organisation’s success. You will draw upon your existing deep understanding and experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

Depending on your experience:

You will both partner with teams across our existing research areas and create and lead standalone projects, bringing together a variety of disciplined scientists and engineers to pursue some of the most ambitious modelling problems through the application of LLMs - as well as providing technical mentorship and people management for others in the ML community at Isomorphic Labs.

You will be instrumental in leading machine learning based research projects, building the models, and algorithms that will power our platform to drive us close to achieving our ambitious mission to one day solve all disease with the help of AI.

What you will do

• Contribute to core research in machine learning by pushing the boundaries of Large Language Models in their application to the world of drug discovery
• Develop and refine LLM-driven approaches for multiple use cases across our research areas, using your extensive knowledge of the field to modify and apply models and explore their capabilities when applied to complex biological and medical challenges.
• Identify and create novel ML techniques and the required data to train and apply models
• Analyse and tune experimental results to inform future experimental directions.
• Implement and scale training and inference engineering frameworks
• Report and present research findings and developments clearly and efficiently, to both other ML scientists and scientists of different disciplines
• Iterate collaboratively with scientists and domain experts, sharing your own domain experience
• Suggest and engage in team collaborations to meet ambitious research goals
  
  Depending on your experience:
• Provide technical mentorship and guidance to the ML research community, advising on projects, and shaping our research roadmap based on your deep technical expertise.
• Provide developmental support to other ML research scientists.
• Create, lead, and run ML research projects, fostering collaborative and diverse teams to solve high priority modelling problems. Cultivate a diverse and inclusive research culture.

Skills and qualifications

Essential

• PhD or equivalent practical experience in a technical field
• Highly specialized LLM builder with a passion for, and strong experience in, applying LLMs to novel problem spaces
• Deep understanding and experience of model architectures, training methods, and techniques for application of LLMs, including deployment for bespoke use cases and applications, established experience in post-training, reasoning, test-time scaling, tool-use, alignment, agents, LLM research, as well as reinforcement learning and fine-tuning
• Strong knowledge of linear algebra, calculus and statistics.
• Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas.
• Depending on your experience: project supervision, leadership, or management.
• Strong knowledge of LLM landscape today, and likely developments for the near future
• Experience working with real-world datasets.

Nice to have

• PhD in machine learning or computer science.
• Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases.
• A strong background in biology or medicine, enabling you to bridge the gap between advanced AI capabilities and complex scientific problems in drug discovery
• Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics), and with real world biological or chemical datasets and biological or chemistry software
• Experience in computational chemistry, bioinformatics, or related fields would be highly beneficial, along with a track record of applying AI to real-world scientific challenges
• Experience with multi-parameter optimization
• Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for computer vision, 3D graphics/robotics, real-world applied RL