Research Engineer - Machine Learning

Research Engineer - Machine Learning

Type: Full-time • Location: Remote (UK/EU based) • Compensation: Competitive (plus equity commensurate with experience)

About us

Bindbridge is pioneering sustainable agriculture through AI-powered molecular glue discovery. We build a computational platform to bring targeted protein degradation to agriculture, with backing from leading VC like Speedinvest and Nucleus Capital. Our first goal is to discover herbicides that revolutionise crop protection while minimising environmental impact.

The role

Join our engineering team to integrate generative AI models into Bindbridge’s discovery and design platform. Work with ML scientists to implement state‑of‑the‑art research, extend open‑source repositories, and transform prototypes into reliable, reproducible, scalable systems across our pipeline.

Key responsibilities

• Implement and productionise ML models—translate research prototypes into robust, maintainable, and tested codebases.
• Design, build, and maintain infrastructure for data ingestion, preprocessing, training, inference, and evaluation.
• Optimise and scale distributed training and inference pipelines across GPUs, clusters, or cloud environments.
• Instrument models and systems with monitoring, logging, and experiment‑tracking tools (Weights & Biases, MLflow).
• Collaborate with research scientists to accelerate experiments, validate results, and ensure reproducibility.
• Set software engineering standards, conduct code reviews, share best practices, and contribute to a culture of technical excellence.

What you will bring

• PhD or MSc in Computer Science, Mathematics, Statistics, or a related technical field (research or industry experience also considered).
• 2+ years of experience in fast‑paced research or engineering environments, ideally as an early‑stage ML or software engineer in a startup.
• Proven expertise in building and managing ML infrastructure for large‑scale training, inference, and deployment.
• Experience navigating and extending complex research codebases, including open‑source frameworks and academic implementations.
• Proficiency in PyTorch and MLOps/DevOps tooling (Weights & Biases, Docker, Kubernetes), with CI/CD (GitHub Actions) and cloud infrastructure (GCP, AWS, or SLURM‑based HPC).
• Strong background in software engineering best practices—testing, monitoring, versioning, and documentation.
• Excellent communication and documentation skills, with a strong bias for reproducibility and collaboration.
• A proactive, delivery‑oriented mindset and a passion for enabling cutting‑edge research through scalable systems.

Nice to have

• Experience building or extending infrastructure for large‑scale training, distributed optimisation, or model evaluation pipelines.
• Familiarity with experiment‑tracking and monitoring frameworks (Weights & Biases, MLflow) and MLOps/DevOps tooling (Docker, Kubernetes, Terraform).
• Knowledge of bioinformatics or molecular simulation software stacks (RDKit, OpenMM, GROMACS, PyRosetta) and their integration into ML workflows.
• Exposure to infrastructure‑as‑code, cloud orchestration, and GPU cluster management.
• Interest in applied AI for science, and a desire to collaborate closely with researchers to turn prototypes into production‑ready systems.

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.

Application process

Our hiring process is clear, efficient, and reflective of how we work:

• CV review – we assess relevant expertise and motivation.
• First interview – an informal conversation with a founding team member to discuss background and interests.
• Second interview – technical interview exploring algorithm design, experimental validation, and translating ideas into working models.
• References & offer – we check references and move quickly to an offer if aligned.

We promise to communicate clearly at every stage, look for strengths, be transparent with feedback, and stay open to your input.