ML Engineer

AI for drug design is limited by sparse and fragmented data. deepmirror is an AI-native drug design platform built around curated, non-public experimental molecule property measurements (potency, binding, ADMET) aggregated from patents, papers, and partners. We help chemistry teams choose the next best experiments to progress programmes faster with fewer dead ends. Since launching in 2023, our platform is now used by hundreds of chemists across the globe to impact real-world drug programmes in oncology, dementia, inflammation, and global health. Now, we are looking for an experienced ML engineer to supercharge our product.

In this role, you will design and develop cutting-edge algorithms for drug design to tackle real-world challenges of AI in drug discovery. This is an outstanding opportunity for someone who wants to be involved from day one of the start-up journey and who wants to put new processes into place to build a powerful platform in a high-performing and collaborative team, based in beautiful Victoria House in the heart of London.

As part of the platform team, you will build and contribute to core services integrating advanced molecular property prediction algorithms into our platform and interface with users to improve their experience. Leveraging your expertise in ML engineering, we encourage you to seize the opportunity to be independent and drive innovation and quality. In the role, you will have substantial growth opportunities, allowing you to shape deepmirror’s technological framework from its inception and learn in an interdisciplinary environment at the interface of physics, chemistry, biology, and machine learning.

You Will:

• Build predictive ML models for molecular property prediction, foundation models, and Auto ML pipelines.
• Build ML infrastructure including training pipelines, experiment tracking, model registry, CI/CD for models.
• Production model serving with a focus on low-latency inference.
• Data pipelines that prepare, validate, and version datasets for training and evaluation.
• Close collaboration with product and customers to ship user-facing features.

Requirements:

• 3+ years of industry experience building and deploying ML systems in production (not just research prototypes).
• Strong software engineering skills.
• Hands-on experience with MLOps tooling experiment tracking and model serving, and containerisation.
• Comfort with cloud infrastructure (AWS, GCP, or Azure) and infrastructure-as-code.
• Strong communication skills and ability to work across disciplines.

Nice to Have:

• Experience deploying ML models into production environments.
• Contributions to open-source scientific software (e.g., RDKit, OpenMM, PyTorch or related tools).
• PhD in chemistry, computational chemistry, or a related physical science / computer science.

If you meet at least 60% of the requirements or nice-to-have qualifications, we encourage you to apply.

Competitive Option Plan in line with the stage of the company. Frequent social events and off-sites. Private medical insurance. 1-week remote working per quarter. Cycle to Work Scheme. Pension Scheme: 5%/5% employer/employee.