Machine Learning Research Scientist

About us

Bindbridge is pioneering sustainable agriculture through artificial intelligence (AI)-powered molecular glue discovery. With backing from leading venture capitalists including Speedinvest and Nucleus Capital, we are building a computational platform to bring targeted protein degradation to agriculture. Our first goal is to discover herbicides that revolutionise crop protection while minimising environmental impact.

The role

We are looking for an experienced Machine Learning Research Scientist to join our engineering team and help advance generative AI models for Bindbridge's molecular glue discovery and design platform.

You will work alongside a team of machine learning (ML) scientists and engineers with experience across Big Tech, startups, and academia. Together, you will explore and extend state-of-the-art architectures — including diffusion-based co-folding, generative modelling, and molecular representation learning — to model protein–molecule–protein interactions that drive molecular glue discovery.

This role combines deep theoretical understanding with hands-on experimentation. You will design and prototype new algorithms, run experiments, and translate promising research into validated methods that advance our discovery pipeline. Collaborating closely with chemists and biologists, you will ensure that model outputs are biologically interpretable and experimentally meaningful.

The ideal candidate has a track record of developing novel ML architectures, adapting research codebases, and bridging the gap between theory and real-world scientific application.

Key responsibilities

• Identify, read, and synthesise emerging research from leading ML labs/conferences/journals in areas such as protein co-folding, generative modelling, probabilistic inference, and molecular representation learning.
• Design and prototype new ML architectures that capture protein–molecule–protein interactions relevant to molecular glue discovery.
• Run and analyse experiments with high scientific rigour — establishing benchmarks, reporting metrics, and refining hypotheses.
• Adapt and extend large research codebases introducing innovations and evaluating their performance.
• Collaborate closely with chemists and biologists to integrate structural and experimental data, ensuring model outputs are interpretable and actionable.
• Communicate results clearly through internal reports, documentation, and publications at leading machine learning and computational biology venues.

What you will bring

• PhD in Computer Science, (Applied) Mathematics, Statistics, or a related technical field. Candidates with significant research or industry experience will also be considered.
• 2+ years of experience in fast-paced research or engineering environments, ideally as a founding or early-stage contributor in a startup or applied research team.
• Expertise in protein co-folding and structure prediction methods and familiarity with building or adapting related data pipelines.
• Strong understanding of generative modelling, probabilistic inference, and molecular representation learning.
• Familiarity with protein sequence and structure data (FASTA, UniProt, PDB, mmCIF, MSA) and molecular representations (SMILES, RDKit).
• Proficiency in PyTorch and supporting data tooling (NumPy, Pandas), with solid software engineering practices (GitHub, CI/CD).
• Comfortable operating in cloud or cluster environments (GCP, AWS, or SLURM-based HPC).
• Proven ability to communicate research clearly through internal reports or publications in top-tier venues such as NeurIPS, ICML, ICLR, JMLR, or similar.
• A strong sense of ownership, curiosity, and drive to translate ML advances into real scientific discovery.

Nice to have

• Familiarity with transformer architectures, graph neural networks, or diffusion models, particularly as applied to molecular or protein structure data.
• Knowledge of bioinformatics or molecular simulation software stacks (RDKit, OpenMM, GROMACS, PyRosetta) and their integration into ML workflows.
• Exposure to ML engineering and DevOps tooling, including experiment-tracking frameworks (Weights & Biases), containerisation and orchestration tools (Docker, Kubernetes), and MLOps/CI/CD workflows for scalable research.

Why join us

• Competitive salary and meaningful equity, commensurate with experience.
• Fully remote work arrangement with quarterly in-person team meetings.
• Support for conference attendance, publications, and patent filings.
• Be part of a founding team shaping a new era of AI-driven agriculture.
• Contribute directly to global food security and environmental sustainability through safer, smarter crop protection.
• Join a culture that values curiosity, rigour, and speed - where transparency, ownership, and collaboration across science and engineering are core principles.