Research Engineer - Machine Learning
bindbridge.com
Software Engineering
United Kingdom · Europe · Remote
Posted on Nov 6, 2025
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 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 Research Engineer to join our engineering team and help integrate generative AI models into 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 take cutting-edge research — implementing state-of-the-art ML papers, extending open-source repositories, and turning prototypes into reliable systems — and make it usable, reproducible, and scalable across our discovery pipeline.
This role combines strong engineering fundamentals with a deep understanding of modern ML workflows, from data preprocessing and experiment tracking to distributed training and large-scale inference. You will take ownership of the experimental infrastructure that accelerates research iteration, enabling our scientists to move rapidly from prototypes to production-ready models and making these models accessible to chemists and biologists.
The ideal candidate has a track record of building robust ML systems, optimising large-scale training pipelines, and bridging the gap between cutting-edge research and deployment.
Key responsibilities
Implement and productionise ML models — translating 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, (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 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 experience in 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 designed to be clear, efficient, and a genuine reflection of how we work:
CV review
We look for relevant expertise and motivation to join an early-stage, mission-driven team.
First interview
A conversation with a team member to learn more about your background and give you a chance to ask questions about Bindbridge.
Second/third interview (technical)
A deep dive into your scientific expertise, including discussion of target selection, assay design, and bioinformatics approaches.
References & offer
We check references, then move quickly to an offer if we are aligned.
We promise to:
Communicate clearly at every stage.
Look for your strengths, not just your gaps.
Be transparent with feedback and open to yours.
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 designed to be clear, efficient, and a genuine reflection of how we work:
CV review
We look for relevant expertise, strong motivation, and alignment with our mission as an early-stage research company.
First interview - Exploratory
An informal conversation with a founding team member to discuss your background, interests, and what excites you about Bindbridge.
Second interview - Technical
A technical interview with our engineering and research team, exploring your approach to algorithm design, experimental validation, and translating ideas into working models.
References & offer
We check references, then move quickly to an offer if we are aligned.
We promise to:
Communicate clearly at every stage.
Look for your strengths, not just your gaps.
Be transparent with feedback and open to yours.