SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
CuspAI is an AI-driven materials discovery company using machine learning to accelerate the discovery of breakthrough materials for energy, clean water, semiconductors, carbon capture, and other critical applications. The company's founding team comprises world-class researchers in AI, chemistry, and engineering.
As a Scientific Product Manager, you will join the product team to onboard partner laboratories onto the CuspAI platform and translate their experimental workflows into product requirements. You will own a portfolio of partner labs, working closely with materials scientists, ML researchers, and platform engineers to make AI-driven experimentation work in real laboratory settings.
Key responsibilities include:
**Product Requirements & Roadmap:** Shape and prioritize the roadmap for how experimental data, protocols, and campaigns integrate into the CuspAI platform. Write detailed requirements, specifications, and acceptance criteria for engineers covering experiment types, instrument integrations, and LIMS/ELN connections. Contribute to decisions on what to build, integrate, or leave to partners' existing systems.
**Partner Lab Onboarding:** Lead technical discovery with partner labs to map their workflows, instruments, sample logic, and data systems. Own the full onboarding process from scoping through first closed-loop campaign execution. Turn each onboarding into reusable patterns and documentation to accelerate future integrations. Serve as the primary scientific counterpart for your partner labs, representing the product in meetings, workshops, and conferences.
**Closed-Loop Campaigns:** Collaborate with ML researchers to design optimization campaigns (e.g., Bayesian optimization) that respect real experimental constraints like throughput, noise, and failed samples. Define validation approaches for platform-driven campaigns, including benchmarks against manual workflows, success metrics, and evidence presented to partners.
The role is based in Amsterdam with an expectation of being in the office three days per week. Regular travel to partner labs and other locations may be required for collaboration and project work.
**Requirements:**
- PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or related experimental discipline, or equivalent research experience
- Hands-on experience with high-throughput or automated experimentation (robotic synthesis, combinatorial libraries, automated characterization, self-driving labs) in industry, national labs, research institutes, or academia
- Proven track record translating scientific or process needs into engineering requirements and validating implementation in laboratory settings
- Firsthand understanding of experimental data production, metadata/provenance requirements, instrument limitations, data quality issues, and ML-ready datasets
- Deep expertise in at least one materials domain (semiconductors/thin films/photovoltaics, catalysis/electrochemistry, energy materials/batteries, polymers/formulations, or separations/surfaces) with curiosity across domains
- Working knowledge of Python for data handling and analysis; comfortable with APIs and data schemas
- Strong communication and collaboration skills, comfortable engaging with external partner labs and multidisciplinary science, ML, product, and engineering teams
**Bonus qualifications:**
- Direct experience with closed-loop optimization (Bayesian optimization, active learning, design of experiments)
- Experience with research data management, LIMS/ELN systems, FAIR data, metadata schemas, or lab data standards (SiLA 2, AnIML, Allotrope)
- Instrument integration experience including vendor software, data formats, and scheduling/orchestration
- Product or project management experience (roadmaps, user research, research collaborations, vendor relationships)
- Industry experience at materials, chemicals, energy, or deep-tech companies, or close collaboration with industry at research institutions or national labs
- Experience coordinating work across multiple labs, sites, or countries
