About Scala

Scala Biodesign is a biotech startup pioneering computational protein design platforms that power the next generation of biologics and industrial enzymes. Built on over a decade of research at the Weizmann Institute of Science, Scala’s technology is trusted by ten of the world’s top twenty pharmaceutical companies and by global leaders in chemical production. Our platform has been used to design antibodies, complex drug targets, and enzymes, including a malaria vaccine candidate currently in Phase II clinical trials.

Our platforms fuse physics-based modeling, AI, and evolutionary data and have been shown in numerous cases to optimize key properties such as potency, manufacturability, and stability under industrially relevant conditions, delivering major performance gains in a single design round and significantly reducing experimental costs.

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 Scala’s computational platform.

Responsibilities

• 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 Bioinformatics, Computational Biology, Physics, Mathematics, or a related field with 2+ years of industry experience, or an M.Sc. with 5+ years of relevant industry experience
• Strong algorithmic thinking and problem-solving skills, with the ability to translate theoretical ideas into practical solutions
• Strong proficiency in Python and PyTorch, with hands-on experience training and fine-tuning deep learning models on GPUs
• 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