We are looking for an Applied AI Researcher to join our multidisciplinary Antibody team composed of computational biologists, mathematicians and protein designers. In this role, you will drive the development and application of advanced machine learning methods to design and optimize antibodies, contributing directly to cutting-edge therapeutic programs and to the evolution of Scalas computational platform.
Develop and apply cutting-edge AI/ML methods for antibody design, including generative modeling and protein language models
Design and implement algorithms for multi-objective optimization of antibodies (e.g., affinity, specificity, stability, developability)
Build tools to support iterative design cycles, combining computational predictions with experimental feedback
Collaborate closely with experimental and computational teams to translate models into impactful design outcomes
Stay current with advances in machine learning, structural biology, and AI-driven protein design
Requirements: Ph.D. in Computer science, Physics, Mathematics, computational biology or related field with 2+ years of industry experience, or an M.Sc. with 5+ years of relevant industry experience.
Strong proficiency in Python and PyTorch, with hands-on experience training and fine-tuning deep learning models on GPUs.
Strong algorithmic thinking and problem-solving skills, with the ability to translate theoretical ideas into practical solutions.
Strong communication and collaboration skills.
Fast learner and proven ability to master new domains.
Advantage: Proven experience developing and applying machine learning models to biological or chemical data. Experience with antibody modeling and design (sequence, structure, or interaction prediction).
Advantage: experience profiling and optimizing GPU workloads.
This position is open to all candidates.