Research Scientist (Machine Learning)

Key Skills

3D Graphics, Aartificial intelligence, AI Robotic systems, Algorithms, Deep Learning, Design, Drug Discovery, JAX framework, Machine learning techniques, NumPy, Pandas, PyTorch, SciPy, TensorFlow

Job Description

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 machine learning (ML), 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 conceive, design, and develop cutting edge 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 experience whilst learning from those around you, to apply novel techniques and ideas to newly encountered computational biology and chemistry problems.

What you will do

• Contribute to our research directions in machine learning by using your extensive knowledge of the field to apply world-leading ML algorithms to drug discovery.
• Identify and create novel ML techniques and the required data to train.
• Develop the architectures and training algorithms of machine learning 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.

Skills and qualifications 

Essential

• PhD or equivalent practical experience in a technical field.
• A proven track record in machine learning using deep learning techniques, including designing new architectures, hands-on experimentation, analysis, and visualisation.
• 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
• 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 proven track record of publications, or contributions to machine learning codebases.
• Scientific knowledge of biology, chemistry, or physics
• 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 3D graphics/robotics, real-world applied RL.