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CuspAI is an AI-driven materials discovery company using machine learning to accelerate the development of breakthrough materials for energy, clean water, computing, and carbon capture. The company's founding team includes world-class researchers in AI, chemistry, and engineering.
You will join the Product team as a Scientific Project Manager, taking ownership of partner laboratory integrations and onboarding. You will work at the intersection of materials science, ML research, and platform engineering to bring experimental workflows onto the CuspAI platform.
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 partner systems.
**Partner Lab Onboarding:** Lead technical discovery with assigned partner labs, mapping their workflows, instruments, sample logic, and data systems. Own the full onboarding lifecycle from scoping through first closed-loop campaign execution. Serve as the primary scientific counterpart for your partner labs, representing the product in meetings, workshops, and conferences. Turn each onboarding into reusable patterns and documentation for the team.
**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.
You will spend significant time with partner teams and their experimental data, translating their actual needs into engineering requirements. The role is based in Singapore with an expectation of three days per week in the office, plus regular travel to partner labs and other locations.
**Requirements:**
- PhD in Materials Science, Chemistry, Physics, Chemical Engineering, or related experimental discipline, or equivalent hands-on research experience
- Hands-on experience with high-throughput or automated experimentation (robotic synthesis, combinatorial libraries, automated characterization, closed-loop labs) in industry, national labs, research institutes, or academia
- Proven track record translating scientific or process needs into engineering requirements and validating implementation in the lab
- Firsthand understanding of experimental data production, metadata and provenance requirements, instrument limitations, data quality issues, and ML-usable 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 others
- 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 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
- Experience coordinating work across multiple labs, sites, or countries
