Principal Research Scientist (Machine Learning), LondonIsomorphic Labs

Principal Research Scientist, Machine Learning, London

Isomorphic Labs is a new Alphabet company that is reimagining drug discovery through a computational- and AI-first approach. 

We are on a mission to accelerate the speed, increase the efficacy and lower the cost of drug discovery. You'll be working at the cutting edge of the new era of 'digital biology' to deliver a transformative social impact for the benefit of millions of people. 

Come and be part of a multi-disciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring, collaborative and entrepreneurial culture.

Your impact

You will be instrumental in leading greenfield machine learning based research projects, building the models, and algorithms that will power our platform to transform the drug discovery world as we know it.

Operating in a highly creative, fast-paced and interdisciplinary environment. Partnering with leading engineers and scientists to conceive, design, and develop pioneering machine learning algorithms to unlock new modelling and predictive power which will be critical to the organisation’s success. You will draw upon your existing deep research and leadership experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology ,chemistry, and medical research problems.

As a senior member of the ML research team, you will be creating and leading projects - bringing together a variety of disciplined scientists and engineers to pursue some of the most ambitious modelling problems with deep learning - as well as providing technical mentorship and people management for others in the ML community at Isomorphic Labs.

What you will do

• Contribute to our research directions in machine learning by using your extensive knowledge of the field to create world-leading ML algorithms for drug discovery.
• 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 people management and developmental support to other ML research scientists.
• Identify and create ML techniques and the required data to train.
• Develop the architectures and training algorithms of machine learning models.
• Analyse and tune experimental results to advise future experimental directions.
• Iterate collaboratively, report and present research findings with scientists and domain experts, sharing your own demonstrated ability.
• Suggest and engage in team collaborations to meet high-reaching research goals.
• 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) and significant further academic or industry research experience in a highly related technical field (machine learning or drug discovery).
• A consistent track record of credible machine learning research using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation.
• Experience with project supervision, leadership, or management.
• Experience with people management.
• Solid understanding 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.
• A passion for applying ML research to real world problems.

Nice to have:

• PhD in machine learning or computer science.
• Relevant research experience to the position such as post doctoral roles, a consistent record of publications, or contributions to machine learning codebases.
• Scientific knowledge of biology, chemistry, or physics,
• Experience working in a scientific environment across fields (particularly biology, chemistry, physics).
• Biological or chemical data and biological or chemistry software with real-world datasets.
• Experience with ML on accelerators.
• Experience in any of: large scale deep learning, generative models, graph neural networks, deep learning for drug discovery, deep learning for 3D graphics/robotics, real-world applied RL.