Senior Scientist, Energy Calculations, Orion Pharma, Cambridge, UK

Orion is seeking a technically skilled and scientifically curious Senior Scientist Free Energy Calculations to join our Molecular Prospecting and Modeling Unit.

This role is ideal for a physicochemically minded expert who wants to deepen and expand the application of absolute and relative binding free energy (ABFE/RBFE) simulations in drug discovery. If you are passionate about advancing physics-based modeling and enabling its widespread, reliable use in small molecule design, we want to hear from you.

We are on a bold digital transformation journey to redefine pharmaceutical R&D through the power of data, machine learning, and AI. As part of this mission, we are building a world-class digital R&D ecosystem that integrates scientific innovation with cutting‑edge technology. We are paving our way to the future by transforming how we discover and design small molecule therapeutics, integrating computational methods with domain expertise in medicinal chemistry, biology, and pharmacology.

Join us at the forefront of this transformation and help accelerate the discovery and development of new therapies for patients around the world.

As Senior Scientist, you will be responsible for ensuring that state‑of‑the‑art free‑energy methods are robustly integrated into Orion’s drug discovery toolbox. Your role combines scientific development and internal consulting: you will both innovate new modeling capabilities and act as an expert enabler for other computational scientists who directly support medicinal chemistry and project teams.

You will work closely with peers across molecular modeling, cheminformatics, and ML/AI, helping elevate how physics‑based approaches are used for hit evaluation, hit‑to‑lead progression, and lead optimization. While your focus will be on binding free‑energy simulations, your scope can include other molecular dynamics and hybrid modeling approaches combining physics and machine learning.

This is a full‑time position based in Cambridge, UK. In this position, you will report to the Head of Computational Design based in Espoo, Finland.

• Applying and refining ABFE and RBFE workflows to support compound optimization decisions in drug discovery
• Evaluating and implementing existing software solutions (e.g., OpenMM, pmx, GROMACS, SOMD, Schrodinger FEP+) for robust and efficient simulation workflows
• Monitoring methodological advances in free energy and hybrid physics/ML modeling approaches, and exploring their applicability
• Collaborating with internal computational scientists to validate models and design production‑ready simulation setups
• Guiding the development of best practices and internal protocols for simulation‑based predictions
• Contributing to scientific publications and engaging with external collaborations where relevant

At Orion, your work directly contributes to improving the lives of patients worldwide. We provide:

• A collaborative and scientifically ambitious environment with top‑tier professionals
• Opportunities to shape how simulation‑based methods guide drug discovery at scale
• Access to high‑quality biological and chemical data and modern computational resources
• Opportunities for publishing and participation in external scientific networks
• Competitive salary, comprehensive health and welfare benefits, and support for international relocation

Our culture of friendliness, respect, mutual appreciation and diversity creates a safe working environment where you can strive for excellence. We offer a wealth of career paths and development opportunities that support the development of innovative solutions and improving the quality of life.

• Ph.D. in computational chemistry, physics, structural biology, or a related field
• 5+ years of postdoctoral experience
• Strong expertise in free energy simulations (ABFE/RBFE) and molecular dynamics
• Hands‑on experience with one or more simulation platforms (e.g., OpenMM, GROMACS, pmx, Schrodinger FEP+, SOMD)
• Understanding of statistical mechanics and force field fundamentals
• Familiarity with hybrid approaches combining physics‑based methods and ML/AI is a strong plus
• Programming and scripting skills…