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.
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
- 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.
Culture and values
What does it take to be successful at IsoLabs? It's not about finding people who think and act in the same way, but we do have some shared values:
Thoughtful at Iso is about curiosity, creativity and care. It is about good people doing good, rigorous and future-making science every single day.
Brave at Iso is about fearlessness, but it’s also about initiative and integrity. The scale of the challenge demands nothing less.
Determined at Iso is the way we pursue our goal. It’s a confidence in our hypothesis, as well as the urgency and agility needed to deliver on it. Because disease won’t wait, so neither should we.
In this together
Together at Iso is about connection, collaboration across fields and catalytic relationships. It’s knowing that transformation is a group project, and remembering that what we’re doing will have a real impact on real people everywhere.
Creating an inclusive company
We realise that to be successful we need our teams to reflect and represent the populations we are striving to serve. We’re working to build a supportive and inclusive environment where collaboration is encouraged and learning is shared. We value diversity of experience, knowledge, backgrounds and perspectives and harness these qualities to create extraordinary impact.
We are committed to equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, pregnancy or related condition (including breastfeeding) or any other basis protected by applicable law. If you have a disability or additional need that requires accommodation, please do not hesitate to let us know.
It’s hugely important for us to be able to share knowledge and establish relationships with each other, and we find it easier to do this if we spend time together in person. This is why we’ve decided to follow a hybrid model, and would require you to be able to come into the office 3 days a week (currently Tue, Wed, and one other day depending on which team you’re in). As an equal opportunities employer we are committed to building an equal and inclusive team. If you have additional needs that would prevent you from following this hybrid approach, we’d be happy to talk through these if you’re selected for an initial screening call.