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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in scientific project delivery, including scoping, execution, and reporting, with a strong focus on machine learning applications in materials science. Proficient in Python for data handling and automation, with a commitment to maintaining clean and structured data workflows.
Highest-signal resume keywords
PhD In Chemistry, Materials Science, Physics, Or Chemical EngineeringAtomistic Simulation ExpertisePython Skills For Data Handling And AutomationExperience With Machine Learning And OptimisationExceptional Communication With Experimental And Computational Scientists
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Atomistic SimulationMachine LearningData-Driven OptimisationExperimental MaterialsHigh-Throughput MethodsCheminformaticsTechnical ReportingWorkflow AutomationData AnalysisSimulation
Soft Skills
Exceptional CommunicationCollaborationProblem-SolvingAdaptabilityPassion For AI In Materials Science
Tools & Technologies
PythonGitData PipelinesAutomation ToolsSimulation Platforms
Industry Keywords
ElectronicsEnergyEnvironmentCross-Cutting ApplicationsAgentic Design
Tech Stack
Tools & technologiesPython
About the role
Key responsibilities & impact- Own scientific delivery of partner projects from scoping to results
- Work with technical programme managers, ML researchers, platform engineers, and the lab integration team
- Ensure the platform answers partner-relevant questions and project learnings feed back into company tools and workflows
- Build cross-cutting capabilities including validated workflows, harmonised data pipelines, and agentic design campaigns
- Lead partner projects from scoping to delivery as a key point of contact alongside technical programme managers
- Write technical scopes defining objectives, deliverables, success criteria, and timelines
- Prepare regular technical reports and presentations for partners and internal teams
- Apply and extend the platform using simulation, machine learning, data-driven optimisation, or experiments
- Design and steer discovery campaigns, including agentic and closed-loop campaigns with partner labs
- Assess model, simulation, and data reliability and help validate them against experiments
- Translate platform recommendations into executable experimental plans and lab realities into internal requirements
- Ensure partner and internal workflow data is clean, structured, and consistent
- Work closely with partner labs, including on-site visits at key project stages
- Build and automate reliable workflows at scale within the platform
- Relay partner feedback to technical programme managers, ML researchers, and platform engineers
- Stay informed about advances in the field and contribute to the scientific direction of the team
Requirements
What you’ll need- A PhD in chemistry, materials science, physics, chemical engineering, or a related field, or equivalent industry experience
- Deep, hands-on expertise in at least one of: atomistic simulation, including machine-learned force fields; experimental materials or chemistry, ideally with automation or high-throughput methods; materials or cheminformatics with machine learning and optimisation
- A track record in one or more of the stated electronics, energy, environment, or cross-cutting application areas
- Python skills for data handling, analysis, automation, or modelling
- Familiarity with version control, including Git
- Exceptional ability to communicate with experimental and computational scientists and translate between them
- Passion for CuspAI's mission and AI's potential in materials science
- London, Cambridge, Amsterdam, or Berlin office location
- Expectation of being in the office at least three days per week
Benefits
Comp & perks- Equity in CuspAI
- 28 days holiday in Germany, the Netherlands, and the UK, or 21 days holiday in Japan, Singapore, and the US, in addition to local public holidays
- 26 weeks of parental leave for primary caregivers at full pay
- 12 weeks of parental leave for secondary caregivers at full pay
- Professional development budget
- Work on meaningful materials science, sustainability, and climate-related problems
- True interdisciplinary teamwork with AI researchers, computational chemists, experimental scientists, world-class researchers, and engineers
- Reasonable workplace adjustments during or after the interview process