Materials AI Product Manager

CuspAI

Amsterdam

Hybrid

EUR 90,000 - 130,000

Full time

6 days ago
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Benefits offered by this job

Competitive salary
Equity in CuspAI
28 days holiday (NL)
Professional development budget
Interdisciplinary teamwork

Job summary

CuspAI is seeking a Scientific Product Manager to bridge AI research, chemistry, and engineering, bringing partner labs and their experimental workflows onto the CuspAI platform. You will own lab integration, translate experimental needs into concrete requirements, and collaborate with ML researchers and engineers to design optimisation campaigns.

You will spend time with partner teams, map workflows, and turn onboarding into reusable patterns.

Qualifications

  • PhD in a materials or related experimental discipline or equivalent research experience.
  • Proven ability to translate lab needs into engineering requirements and validate results.
  • Hands-on experience with high-throughput or automated experimentation in industry, lab or academia.
  • Strong understanding of experimental data provenance and metadata importance for ML.
  • Experience working with multiple labs or sites and communicating across functions.

Responsibilities

  • Own onboarding and integration of partner labs, from scoping to first closed-loop campaigns.
  • Define requirements, acceptance criteria and collaboration with ML and engineering teams.
  • Design and manage Bayesian or other optimisation campaigns within real experimental constraints.
  • Represent the product in partner meetings, workshops and at conferences.

Skills

Python data handling
API familiarity
Data schemas
Communication
Cross-functional collaboration

Education

PhD in Materials Science, Chemistry, Physics or related

Tools

LIMS/ELN systems
Lab automation tooling
APIs

Job description

CuspAI is seeking a Scientific Product Manager to bridge AI research, chemistry, and engineering, bringing partner labs and their experimental workflows onto the CuspAI platform. You will own lab integration, translate experimental needs into concrete requirements, and collaborate with ML researchers and engineers to design optimisation campaigns.

You will spend time with partner teams, map workflows, and turn onboarding into reusable patterns.

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