We are partnered with an early-stage TechBio company building an AI-driven molecular discovery platform to transform sustainability in agriculture. Backed by leading deep-tech investors, the company applies modern machine learning to targeted protein degradation concepts, with the goal of developing next-generation herbicides that improve crop protection while minimising environmental impact.

The role

We are hiring a Research Engineer (Machine Learning) to help integrate generative AI models into the company’s molecular discovery platform. Working within a multidisciplinary engineering team, including ML scientists and engineers from major tech companies, startups, and academia you will take cutting-edge research and translate it into scalable, reliable systems. You will implement state-of-the-art ML papers, extend open-source frameworks, and convert prototypes into production-ready components that enable fast and reproducible scientific iteration.

This role requires strong engineering fundamentals and a deep understanding of modern ML workflows, including data preprocessing, experiment tracking, distributed training, and large-scale inference. You will own the experimental infrastructure that accelerates research, enabling scientists to move from idea to validated model efficiently, while making these tools accessible to chemists and biologists.

The ideal candidate has hands-on experience building robust ML systems, optimising large-scale training pipelines, and bridging research with real-world deployment.

Key responsibilities

• Implement and productionise ML models by transforming research prototypes into well-structured, maintainable, and tested codebases.
• Design, build, and maintain infrastructure for data ingestion, preprocessing, training, inference, and evaluation.
• Optimise distributed training and inference pipelines across GPUs, clusters, and cloud environments.
• Add monitoring, logging, and experiment-tracking using tools such as Weights & Biases or MLflow.
• Collaborate closely with research scientists to accelerate experimentation and ensure reproducible results.
• Contribute to engineering best practices, including code reviews, documentation, and technical standard-setting.

What you will bring

• PhD or MSc in Computer Science, Mathematics, Statistics, or a related technical field (or equivalent experience).
• 2+ years of experience in fast-paced research or engineering settings, ideally in early-stage environments.
• Proven expertise building ML infrastructure for large-scale training, inference, and deployment.
• Experience extending complex research codebases, including open-source or academic implementations.
• Strong proficiency in PyTorch and MLOps/DevOps tooling (Weights & Biases, Docker, Kubernetes), with experience in CI/CD (e.g., GitHub Actions) and cloud/HPC systems (AWS, GCP, SLURM).
• Solid software engineering fundamentals (testing, monitoring, version control, documentation).
• Excellent communication skills with a focus on clarity, reproducibility, and collaboration.
• A proactive, delivery-oriented mindset and passion for enabling research through scalable systems.

Nice to have

• Experience building or extending infrastructure for large-scale training, distributed optimisation, or model evaluation.
• Familiarity with experiment tracking, monitoring, and orchestration frameworks (W&B, MLflow, Docker, Kubernetes, Terraform).
• Knowledge of bioinformatics or molecular simulation tools (RDKit, OpenMM, GROMACS, PyRosetta).
• Exposure to infrastructure-as-code, GPU cluster management, or cloud orchestration.
• Interest in applied AI for scientific discovery and close collaboration with research teams.

Why join

• Competitive salary and meaningful equity.
• Fully remote with quarterly in-person team meetings.
• Support for conferences, publications, and patent filings.
• Opportunity to contribute as an early team member shaping core technology in a rapidly growing TechBio organisation.
• Direct impact on global sustainability and food security.
• A culture valuing curiosity, rigour, ownership, transparency, and collaboration.