Research Engineer - Machine Learning

About us

Bindbridge is pioneering sustainable agriculture through AI-powered molecular glue discovery. Backed by Speedinvest and Nucleus Capital, we are building a computational platform to bring targeted protein degradation to agriculture.

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 ML scientists and engineers, implementing research prototypes into robust, scalable systems.

Key responsibilities

• Implement and productionise ML models by translating research prototypes into robust, maintainable, and well-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, and cloud environments.
• Add monitoring, logging, and experiment-tracking to models and systems using tools such as Weights & Biases and MLflow.
• Collaborate with research scientists to accelerate experiments, validate results, and ensure reproducibility.
• Contribute to engineering standards, perform code reviews, share best practices, and support a culture of technical excellence.

What you will bring

• PhD or MSc in Computer Science, (Applied) Mathematics, Statistics, or a related technical field.
• 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 and cloud infrastructure (GCP, AWS, or SLURM-based HPC).
• Strong background in software engineering best practices, including 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).
• 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, fully remote work, support for conference attendance, publications, and patents. Be part of a founding team shaping AI-driven agriculture and contribute to global food security.

Application process

1. CV review — We look for relevant expertise, strong motivation, and alignment with our mission.
2. First interview (exploratory) — Informal conversation with a founding team member to discuss background and interests.
3. Second interview (technical) — Technical interview with engineering and research team on algorithm design and experimental validation.
4. References and offer — References checked, then offer extended if aligned.