Senior ML Research Engineer

Senior Machine Learning Research Engineer to drive research and development of machine learning models for molecular and structural biology. This is a hands-on role at the intersection of foundation models, structural biology, and federated learning, executing research projects and turning ambitious scientific goals into frontier ML models for real drug-discovery workflows.

What you'll do

• Develop and improve ML models in molecular and structural biology, such as co-folding and binding affinity models, for drug design applications and workflows, driving them from ideation through prototyping iterations to robust tooling.
• Build effective benchmarking and evaluation strategies for model evaluation and iterate and refine existing modelling approaches based on data-driven insights.
• Diagnose and resolve data quality and pipeline issues that affect model quality.
• Stay up to date with a rapidly evolving research literature and identify best public approaches to aid in research and development.
• Collaborate with customers, partner-facing engineers, and external collaborators to support real-world drug design use cases.

What you'll need

• PhD or MSc in machine learning, computational biology, computational chemistry, bioinformatics, physics, or a related field, with at least 2 years of professional experience applying ML to scientific problems.
• Hands-on experience training, fine-tuning and extending deep learning models for molecular or protein structure modelling.
• Ability to rigorously interrogate ML models, their training, and scientific benchmarks, and translate insights into impactful improvements.
• Expert in Python and PyTorch, can produce reliable and clean code, and comfortable with multi-GPU and distributed training.
• Deep familiarity with structural biology and protein–ligand data formats, quality metrics and tooling.

Nice to have

• Experience in federated learning, privacy-preserving ML, or secure model training.
• Experience developing ML models for drug design in pharmaceutical or biotech environments.
• Published at top-tier ML or structural biology venues or contributed to open-source projects in that space.

What they offer

• Industry-competitive compensation, including early-stage virtual share options
• Remote-first working – work where you work best
• Wellbeing budget, mental health benefits, work-from-home budget, co-working stipend and learning and development budget
• Generous holiday allowance
• Office Days at Berlin HQ or European location (3x a year)