In this role, you will report to the Head of the Protein and Vector Engineering (PVE) team and work closely with experienced scientists who provide continuous scientific and technical mentorship. This collaborative environment is designed to uphold high scientific rigor and quality, while progressively fostering your technical autonomy and leadership. Through your work, you will help accelerate the development of genomic medicines, enabling innovative solutions to reach patients faster. Beyond advancing our mission, you will actively contribute to shaping the future of genomic medicine and making a tangible impact on patients’ lives.

Here’s How You’ll Make an Impact :

• Push the boundaries of computational drug design by developing and applying advanced methods in 3D structure prediction, molecular dynamics, free energy calculations, and protein engineering.

• Leverage AI/ML approaches to streamline and elevate discovery workflows, transforming cutting-edge science into real therapeutic breakthroughs.

• Design, build, and scale high-impact computational pipelines that integrate structural, chemical, and genomic datasets, turning complex data into actionable insight.

• Collaborate closely with cross-functional teams to support discovery and optimization projects from concept through execution.

• Shape and improve next-generation internal tools and benchmarks, using state-of-the-art platforms such as Schrödinger, Rosetta, PyMOL, AlphaFold, and related molecular modelling technologies.

• Translate complex scientific results into clear, compelling insights, communicating effectively with both technical experts and non-technical stakeholders to drive informed decisions and outcomes.

We’re Eager to Meet You If:

• You hold a PhD or a Master’s degree in Structural Bioinformatics, Computational Biology, Machine Learning, Computational Chemistry, Genomics, or a related field, with strong hands-on experience.

• You have a solid foundation in molecular modelling, structural biology, and computational chemistry, and proven experience in computational drug design (small molecules and/or biologics).

• You are comfortable working with 3D structure prediction, molecular dynamics, antibody design, and AI/ML approaches applied to drug discovery.

• You code in Python, R, or Bash using Git/GitHub. Experience with tools such as Schrödinger, MOE, Rosetta, PyMOL, AlphaFold, as well as exposure to HPC/cloud environments, peptide modelling, reproducible research workflows, and end-to-end drug discovery pipelines, will be considered strong assets.

Our recruitment process includes the following steps:

• A technical interview with a member of the team to assess scientific and technical expertise.

• A People & Culture interview, focused on values, culture, and overall fit with our working environment.

• An on-site interview, providing the opportunity to meet the team and gain deeper insight into our environment.

• A behavioral assessment (Predictive Index) to better understand working styles and collaboration drivers.

• A reference check, conducted at the final stage of the process.