Scientific product manager in materials discovery

CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering. We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock.

Shape and prioritise the roadmap for how experimental data, protocols and campaigns get into the CuspAI platform, together with your fellow Scientific Product Managers, the engineering lead and the wider product team. Write requirements, specifications and acceptance criteria that engineers can build from, covering experiment types, instrument integrations and LIMS/ELN connections, and work with the data team on the underlying data models and provenance.

Contribute to decisions on what we build, what we integrate, and what we leave to partners' existing systems. Lead technical discovery with the partner labs you take on: map their workflows, instruments, sample logic and data systems, and pin down the true experimental unit and the parameters that matter. Own onboarding for your partner labs from scoping to the first closed-loop campaign, and turn each onboarding into reusable patterns and documentation shared across the team so the next one is faster.

Act as the main scientific counterpart for the partner labs you look after, and represent the product in partner meetings, workshops and conferences. Work with ML researchers to design optimisation campaigns (e.g. Bayesian optimisation) that respect real experimental constraints such as throughput, noise and failed samples. Define how platform-driven campaigns are validated, including benchmarks against manual or legacy workflows, success metrics and the evidence we bring to partners.

• PhD in Materials Science, Chemistry, Physics, Chemical Engineering or a related experimental discipline, or equivalent experience in scientific research.
• Hands-on experience with high-throughput or automated experimentation (e.g. robotic synthesis, combinatorial libraries, automated characterisation, or self-driving / closed-loop labs) in industry, a national lab or research institute, or academia.
• Proven track record of translating scientific or process needs into requirements for engineers, software teams or automation vendors, and of validating that what was built works in the lab.
• Firsthand understanding of how experimental data is produced and where it fails: which metadata and provenance matter, instrument quirks, data quality issues, and what makes a dataset usable for machine learning.
• Depth in at least one materials domain (e.g. semiconductors and thin films including photovoltaics, catalysis and electrochemistry, energy materials including batteries, polymers and formulations, or separations and surfaces) and the curiosity to work across others.
• Working knowledge of Python for data handling and analysis; comfortable working with APIs and data schemas.
• Strong communicator and a proven collaborator, comfortable working directly with external partner labs as well as multidisciplinary science, ML, product and engineering teams.
• Будет плюсом:
  Direct experience with closed-loop optimisation in an automated setting, such as Bayesian optimisation, active learning or design of experiments
• Experience with research data management, LIMS/ELN systems, FAIR data or metadata schemas, and familiarity with lab data…