Machine learning scientist

Scindo is building the next generation of enzyme-powered chemistry by leveraging AI-powered enzyme discovery and design to revolutionize sustainable manufacturing through advanced biocatalysts. By offering unprecedented control over selectivity, our solutions create innovative synthesis routes that reduce energy consumption, minimize waste, and decrease reliance on fossil feed stocks. Our enzymes enable direct conversion of natural, renewable, or upcycled materials into bioactive ingredients found in everyday products, such as cosmetics, personal care items, and food.

At Scindo, we are committed to transforming industrial chemistry for a sustainable future.

You will design, implement, and iterate on machine learning models for enzyme function prediction and generative protein design. Working closely with our experimental team, you will help translate model outputs into testable designs, analyse results, and feed experimental data back into the next round of model development. You will also contribute to mining and curating our proprietary enzyme dataset to extract novel functional signals that drive the platform forward.

The role is based in our office and lab in central London.

• PhD (or equivalent) in statistics, machine learning, applied mathematics, computational biology/chemistry, or computer science.
• Strong foundations in probabilistic modelling and Bayesian inference, including Gaussian processes, variational inference, or uncertainty quantification.
• Experience designing and running active learning loops using Bayesian optimisation and probabilistic modelling, in settings where experimental throughput is the constraint.
• Strong programming skills in Python; experience with PyTorch or JAX preferred.
• Ability to work independently while contributing effectively to a multidisciplinary team.
• Experience with multi-task and multi-objective learning frameworks.
• Familiarity with probabilistic graphical models, hierarchical Bayesian models, or structured priors for scientific data.
• Experience with Monte Carlo methods, MCMC sampling, or stochastic variational inference.
• Familiarity with statistical learning theory, generalisation bounds, PAC learning, or information-theoretic approaches to model selection.
• Experience applying dimensionality reduction or latent variable models (VAEs, factor analysis, probabilistic PCA) to high-dimensional biological data.
• Familiarity with neural force fields or QM/ML hybrid approaches.
• HPC / GPU cluster experience, distributed training, performance optimisation.

• The opportunity to work at the frontier of ML-driven enzyme design, with direct impact on real-world industrial chemistry.
• An exciting active feedback environment: model predictions are tested in-house by our wet lab, and you will work closely with experimentalists to design the experiments that make the models better.
• The chance to join a top interdisciplinary team and make a meaningful contribution to a rapidly developing platform.

If you are a curious problem solver, with a strong drive - we want to hear from you!