Research Scientist, Quantum Chemistry and Machine Learning, London

Research Scientist, Quantum Chemistry and 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 

As a Research Scientist in Quantum Chemistry and Machine Learning you will play an exciting role in building greenfield machine learning based models and algorithms that will power our platform 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 engineers and scientists to simulate biochemistry on a computer thus helping us develop cutting edge predictive models which will be critical to the organisation’s success. You will draw upon your existing deep research experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

You will be instrumental in leading computational chemistry based research projects, predicting relevant experimental quantities and helping to generate data to train algorithms that will power our platform to transform the drug discovery world as we know it.

What you will do

• Contribute to our drug development projects by using your extensive knowledge of theoretical chemistry to run accurate and relevant simulations.
• Develop the computational infrastructure to run simulations at scale through our cloud compute platform.
• Generate synthetic datasets that help improve the performance of our ML models.
• 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.
• A proven track record in applying computational chemistry to predict relevant experimental  quantities in an applied research field.
• Strong scientific knowledge of biology, chemistry, or physics
• Exposure to applied ML research.
• Depending on your experience: project supervision, leadership, or management.

Nice to have

• PhD in chemistry or physics.
• Relevant research experience to the position such as post doctoral roles, a proven track record of publications, or contributions to machine learning codebases.
• Experience using ML frameworks such as JAX, PyTorch, or TensorFlow, and scientific software such as NumPy, SciPy, or Pandas
• Strong knowledge of linear algebra, calculus and statistics
• Experience working in a scientific environment across disciplines (particularly biology, chemistry, physics)
• Experience working with biological or chemical data and biological or chemistry software
• Experience working 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 computer vision, 3D graphics/robotics, real-world applied RL.