Computational Chemist/Engineer
About the Role
We are looking for an experienced Computational Chemist/Engineer to help build physics-based modelling capabilities for the best GPCR drug discovery platform with state of the art AI and Physics tools.
The role sits in a start-up-like team within an established pharma company, combining hands-on delivery, scientific depth, and the opportunity to shape tools used across discovery projects.
You will work in a fast-paced environment to develop robust, reusable workflows for molecular
simulation, free energy calculations, structure-based design, and compound prioritisation.
Responsibilities
- Develop and deploy computational chemistry workflows within a cloud-based platform environment.
- Apply physics-based methods, including molecular dynamics, free energy calculations, docking, and structure-based modelling, to guide compound design and prioritisation.
- Build robust, reusable tools for simulation setup, analysis, reporting, and decision support
- across small molecule discovery programs.
- Use cheminformatics workflows for compound analysis, library design, and property prediction.
- Collaborate with machine learning scientists, software engineers, and platform teams.
- Translate scientific requirements into practical, user-facing computational capabilities.
- Communicate modelling results clearly to multidisciplinary project teams.
- Contribute to engineering standards, reproducible workflows, and best practices for computational chemistry delivery.
Qualifications
Required
- PhD in Computational Chemistry, Chemistry, Medicinal Chemistry, Biophysics, Computational Biology, or a related field; or equivalent industry experience.
- Strong grounding in molecular modelling, structure-based drug design, and small molecule discovery.
- Practical experience with molecular dynamics and/or free energy methods, preferably using tools such as OpenMM, GROMACS, AMBER, FEP, or related platforms.
- Experience with docking, virtual screening, protein-ligand interaction analysis, and compound prioritisation.
- Proficiency in Python, scientific computing, and cheminformatics toolkits such as RDKit.
- Experience developing maintainable scientific code, workflows, or tools for use by other scientists.
- Familiarity with cloud-based compute, containerised workflows, scalable infrastructure, or workflow orchestration.
- Strong problem-solving skills, scientific judgement, and ability to work independently in a fast-moving environment.
- Excellent communication skills and experience working across scientific and technical teams.
Nice to Have
- Industry experience supporting small molecule drug discovery programs.
- Experience building scientific software, workflow automation, APIs, notebooks, or user facing tools for shared research platforms.
- Deeper expertise in OpenMM, FEP, TI, enhanced sampling, MM-GBSA, or related physics- based modelling approaches.
- Experience with high-performance or cloud computing, containers, and reproducible workflow systems.
- Experience applying machine learning or molecular AI to complement physics-based modelling and compound design.
- Experience working in start-up, scale-up, incubator, or platform-building environments.
- Experience using LLM’s within standard workflow practices
