Scientific Product Manager

CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. 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. 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. This role could be based in any of our locations with the expectation of being in the office three days per week, with regular travel required to partner labs and other locations.

Responsibilities

Shape and prioritise the roadmap for how experimental data, protocols and campaigns get into the CuspAI platform; 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 to build, what to integrate, and what to leave to partners' existing systems; Lead technical discovery with partner labs, mapping their workflows, instruments, sample logic and data systems; Own onboarding for partner labs from scoping to the first closed-loop campaign, and turn each onboarding into reusable patterns and documentation; Act as the main scientific counterpart for partner labs 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; Define how platform-driven campaigns are validated, including benchmarks against manual or legacy workflows, success metrics and the evidence brought to partners

Requirements

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

Nice to have

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; 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

Benefits

A competitive salary; Equity in CuspAI; 28 days holiday (DE, NL, UK) or 21 days holiday (JP, SG, US), in addition to local public holidays; 'Gold Standard' parental leave: 26 weeks (primary caregiver) and 12 weeks (secondary caregiver) at full pay; Professional development budget; Solve meaningful problems with direct impact on materials science and sustainability and climate-related problems; True interdisciplinary teamwork bridging AI research, computational chemistry, and experimental science