Bind Research

Bind Research is an innovative not-for-profit research organisation at the forefront of developing tools and datasets to characterise small-molecule interactions with intrinsically disordered proteins. Based just a short walk from Kings Cross station, Bind leverages interdisciplinary methods that span experimental biophysics - with a strong focus on nuclear magnetic resonance (NMR) spectroscopy - as well as computational approaches and cellular studies. You will play a crucial role in shaping the future of this cutting-edge research initiative. Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins. If you are dedicated to supporting the advancement science and technology in an innovative environment, we encourage you to apply! Role Overview We are seeking a Machine Learning Engineer to advance data-processing and model-building and deployment capabilities at Bind. This role includes developing new machine-learning models for highly complex and heterogeneous scientific data such as from nuclear magnetic resonance (NMR), deploying and productionizing these models internally and externally, contributing to open-source software, and large-scale data analysis, curation, and pipeline building. Key Responsibilities Develop innovative machine learning approaches to elucidate and quantify the interactions between small molecules and intrinsically disordered proteins Integrate molecular simulations and deep learning approaches using cutting-edge architectures Software Engineering Enhance the usability of built models by implementing automated, streamlined, and efficient software solutions in line with best practices Utilise active learning, Bayesian, and bootstrapping methods to achieve robust performance in low-data regimes, and make use of distributed training methodologies for large models Build model-deployment and job-launching systems for internal and external use Collaborate closely with other computational and NMR team members, in addition to experimental biophysicists, assisting with experimental data handling and curation Assist in optimising data collection practices in both computational and experimental teams Mentor and support Bind's interdisciplinary team in machine-learning and data analysis methods Driving Innovation Stay current with breakthroughs in machine learning, neural networks, NMR, and computational technologies Contribute to the design and execution of cutting-edge machine learning and NMR research projects that advance Bind's scientific mission Thrive in a dynamic, start-up-style environment where initiative and flexibility in your role are valued. Qualifications and Expertise We encourage applications from software engineers, scientists, and individuals with relevant transferable skills who are enthusiastic about our mission to make disordered proteins druggable, even if they do not meet every requirement listed below. We believe innovation thrives through diverse perspectives and welcome candidates from both academic and industry backgrounds. Education and Experience MSc in a technical field with 3 years of machine learning or model-building experience or a PhD with a similar focus Extensive knowledge of machine learning approaches, neural network architectures, training methods, and data preparation best practices Experience in applying machine learning and modelling techniques to graph-based data such as molecules and proteins, as well as time series Strong dev-ops skillset, with proficiency in model deployment, versioning, distributed architectures, and containerization Track record of completed scientific software projects or open-source project contributions Skills and Abilities Strong written and verbal communication skills, with the ability to communicate effectively with team members in diverse fields Strong programming abilities in Python, and extensive experience with the scientific and machine-learning stack: Numpy, Torch/Tensorflow/Jax, Scikit-learn, Polars, SQL Expertise with deep learning approaches such as diffusion or flow matching Proficiency in modern software development practices: code testing, documentation, packaging and deployment, version control using Git, containerization Proven ability to process, analyse, and present large and complex datasets using techniques such as clustering and dimensionality reduction Additional Attributes A collaborative mindset and an enthusiasm for interdisciplinary teamwork A strong engineering mindset: you believe ease-of-use, reproducibility, maintainability, and clear documentation are key requirements for scientific software and allow complex projects to gain results faster Dedication to continuous professional development in machine learning, dev-ops, programming, and a willingness to learn more about experimental biophysical methods Passion for contributing to the establishment and growth of a world-class not-for-profit research organisation Nice to Have Knowledge of NMR spectroscopy and associated data processing pipelines Familiarity with simulation techniques such as molecular dynamics or Monte Carlo approaches, as well as an understanding of statistical mechanics and complex systems Ability to use HPC and / or cloud computing and building automation and orchestration systems for these platforms Proficiency in a low-level language such as C, C++, or Rust and in GPU frameworks like CUDA Competence in front-end web design to allow easy interfacing with large datasets Our Culture Follow the science. We prioritise rigorous scientific inquiry, relying on evidence and expertise to guide decisions and actions, incorporating the latest research to achieve meaningful, ethical, and impactful outcomes for the public and scientific community. Think dynamically. We believe the most effective solutions come from a dynamic, adaptable mindset that embraces uncertainty as a catalyst for discovery, encouraging creativity, challenging assumptions, and approaching problems from multiple angles to foster innovation, navigate complexity, and deliver exceptional results. Celebrate a diverse ensemble. We celebrate diversity and inclusion, fostering a culture where all perspectives, backgrounds, and talents are valued, respected, and empowered to thrive, enabling us to better understand our community, collaborate effectively, and deliver impactful solutions. Build an innovation hub. We strive to advance disordered protein research by creating and sharing tools and datasets collaboratively, building on past contributions, and working alongside the disordered protein community to deepen understanding and maximise collective impact. What We Offer 38 days holiday (inclusive of bank holidays) Employer pension contribution in line with market standards Cycle to work scheme Life insurance Additional information The interview process will begin with a phone screen. Successful candidates will then be invited to more comprehensive technical and cultural interviews. To apply send your CV and cover letter to with the reference number BRJ017 and your name in the email header. Join Bind Research and help push the limits of drug discovery for intrinsically disordered proteins! We are committed to protecting your personal information. For full details on how we collect, use, and store your data during the recruitment process, please refer to our Privacy Notice .

A not-for-profit research organization in Greater London is seeking a Machine Learning Engineer to develop innovative models for drug discovery involving intrinsically disordered proteins. This role includes enhancing data processing capabilities, deploying models, and collaborating with experimental teams. Candidates should have an MSc or PhD, experience in machine learning, and strong programming skills, particularly in Python. Attractive perks include extensive holiday and a pension scheme.