AI / ML Research Scientist (Drug Discovery / Biotech)

Location: Munich, Germany

Department: AI & Computational Discovery

Salary Range: €90,000 – €130,000+, depending on experience

Overview of AI / Machine Learning Research Scientist

Eblex Group are supporting with the search for an accomplished AI / Machine Learning Research Scientist with proven experience in a biotechnology or pharmaceutical R&D environment. You will design and implement AI models that accelerate drug discovery — from target identification to molecule design — by leveraging high-quality scientific data and close collaboration with experimental teams.

Key Responsibilities of AI / Machine Learning Research Scientist

• Develop and apply machine learning models to support small molecule and biologics discovery, including generative chemistry, target prediction and structure–activity relationship modelling.
• Conduct end-to-end ML research, from data exploration and feature engineering through to model validation and deployment.
• Apply deep learning architectures such as graph neural networks, transformers, and diffusion models to biological and chemical data.
• Collaborate with data engineers and bioinformaticians to ensure high-quality, curated data availability for model development.
• Interpret and communicate model outputs clearly to discovery scientists, chemists and leadership teams.
• Evaluate and implement emerging AI techniques, keeping the organisation at the cutting edge of computational discovery.
• Partner with wet-lab scientists to validate model predictions experimentally.
• Maintain detailed documentation of models and workflows to ensure reproducibility and regulatory compliance.

Required Experience & Skills of AI / Machine Learning Research Scientist

• PhD or Master’s degree in Computer Science, Computational Biology, Bioinformatics, or a related discipline.
• 3–6 years of professional experience applying AI/ML in a biotech or pharmaceutical context (mandatory).
• Demonstrable experience in AI-driven drug discovery, computational chemistry, or predictive biology.
• Strong proficiency in Python, and experience with frameworks such as PyTorch, TensorFlow, and scikit-learn.
• Proven ability to manage and model large-scale omics or chemical datasets.
• Familiarity with GxP data environments, version control (Git), and model documentation standards.
• Excellent problem-solving, collaboration and communication skills, with the ability to work cross-functionally.

Desirable

• Publications or conference presentations in AI for drug discovery, computational biology or chemistry.
• Experience with multimodal learning (e.g. integrating omics, imaging, and text).
• Knowledge of MLOps frameworks (MLflow, Weights & Biases) for reproducible deployment.