Scientific Product Manager

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.

We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters.

We’re on the cusp of the on-demand materials era. Join us.

Due to rapid growth in our partner lab network, we are seeking a Scientific Product Manager to join our Product team and bring partner labs and their experimental workflows onto the CuspAI platform.

This is an exciting opportunity to join the product team at CuspAI, working closely with materials scientists, ML researchers and platform engineers to make AI-driven experimentation work in real laboratories. As one of a small team of Scientific Product Managers, you will take ownership of a set of partner labs and the lab integration work around them, translating what each lab actually needs into requirements our engineers and ML researchers can build against. Alongside the product work, you will spend real time with your partners' teams and their experimental data.

• 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.

• 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.

• 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.

• 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 standards (e.g. SiLA 2, AnIML, Allotrope).

• Instrument integration experience, including vendor software, data formats, and scheduling or orchestration.

• Product or project management experience, such as roadmaps, user research, external research collaborations or vendor relationships. We are equally open to strong experimental scientists making their first move into product.

• Experience working in industry at a materials, chemicals, energy or deep-tech company, or closely with industry at a research institution or national lab.

• Experience coordinating work across multiple labs, sites or countries.

This role could be based in any of our locations with the expectation of being in the office three days per week. Additionally, there may be regular travel required to partner labs and other locations for collaboration and project work.