Should I apply? We want to be upfront about what it is like to work at deepmirror and thought hard about the principles that guide our work. Before you apply, let's dive into how our values influence the way we work as a team and ensure they resonate with you.

We Persevere: We believe that great work comes from dedication, continuous learning, and pushing boundaries. We trust you to manage your time in a way that helps you develop your skills while contributing to impactful projects. If you thrive on learning and enjoy challenging yourself, you'll fit right in.

We Care We love what we do and deeply care about our product, customers, and colleagues. We thrive as a collaborative team where everyone is willing to go the extra mile for our customers. We create an environment where asking for support and extending a helping hand are equally valued. Be part of something big and contribute to a culture of support, customer focus, and shared ambition.

We Own Nobody will dictate how you do things, but you will be held accountable for the impact of your work, as we value outcomes over outputs. If you thrive in an environment where you take responsibility, solve problems proactively, and drive your own success, you will do well here.

At deepmirror, you will challenge yourself, be supported, and be given the freedom to excel. Join a team where striving, caring, and ownership are not just values but a way of life. If this resonates with you, deepmirror could be your next big adventure—read on for the 'boring' bits.

In this internship, your primary focus will be on intelligent compound selection for ADMET modelling specifically, how to choose the smallest, most informative set of compounds to measure in order to build predictive models that generalise well across a defined chemical space. The role combines rigorous benchmarking with practical impact, as you develop and evaluate strategies that maximise model performance under realistic experimental budget constraints.

As part of our product-focused team, you will bridge the gap between academic research and industrial applications, translating insights from active learning and cheminformatics into practical guidance for drug development workflows. We maintain a strong commitment to scientific excellence and actively support publication of research findings in peer-reviewed journals.

You will:

• Design and benchmark compound acquisition strategies, including diversity-based, uncertainty-based, and hybrid active learning approaches
• Evaluate model generalisation across ADMET endpoints in realistic low-data settings
• Investigate how data quality interventions (e.g. removal of activity cliffs, outliers, or noisy datapoints) affect predictive performance
• Publish results in a scientific journal

Requirements

• Strong understanding of cheminformatics and machine learning, with practical experience applying these to real-world problems
• Experience in Python, RDKit, and PyTorch
• At the time of internship, you must be enrolled as a Ph.D. student at a University
• Willingness to work in-person in London for the duration of the internship

Nice to Have:

• Experience curating and preparing datasets for computational chemistry or ADMET modelling
• Familiarity with active learning methods or Bayesian optimisation
• Familiarity with QSAR/ADMET modelling and molecular property prediction
• Experience working with public chemical databases (ChEMBL, PubChem)

If you meet at least 60% of the requirements or nice-to-have qualifications, we encourage you to apply.

Benefits

• Competitive salary - paid internship
• Social events
• Central London Offices