Scientific Developer Relations Manager
About this role
CuspAI is focused on solving complex materials challenges using frontier AI to unlock breakthroughs in areas such as energy, clean water, and carbon capture. The company aims to accelerate the discovery of next-generation materials by harnessing AI to achieve results in months rather than millennia. The team is composed of world-class researchers across AI, chemistry, and engineering, operating at the intersection of AI and materials science.
This Scientific Developer Relations Manager role is central to scaling the platform's adoption among enterprise and research partners. The core function is to build the technical enablement and scientific advisory framework necessary to bring CuspAI's AI platform to market for advanced materials.
This position requires an individual who can effectively bridge complex materials science requirements with actionable platform solutions. It exists to ensure that external partners can successfully leverage the platform, driving innovation and industry collaboration in the materials sector.
What you will do
- The manager will serve as the primary technical and scientific contact for technical leaders and scientists within partner organizations.
- This involves providing expert, scientific advice on how to use the CuspAI platform to address specific material challenges.
- Responsibilities include leading technical workshops, code-alongs, and scientific demonstrations for external audiences.
- The role requires building scalable developer resources, such as technical documentation, sample notebooks, and APIs.
- A key function is bridging communication between community events and the hands-on technical support needed for partner Discovery Sprints.
- The manager will collaborate with internal research and product teams to incorporate technical partner feedback into platform development.
What you need
- Candidates must possess a strong academic background or equivalent professional experience in Materials Science, Chemistry, Physics, or Electrical Engineering.
- Fluency in communicating at a peer level with world-class scientists is necessary for technical advisory functions.
- Hands-on experience with computational chemistry tools, molecular simulation software, or Python-based data science workflows is required.
- A proven track record in developer relations, technical partner engineering, or application science within a deep-tech environment is essential.
- The ideal candidate must be a confident technical communicator capable of translating complex scientific concepts into clear, actionable advice.
- A genuine interest in how frontier AI can revolutionize materials science is required.
Nice to have
- Experience managing joint developer or research ecosystem programs alongside major hardware tech giants
- Direct professional background working within a semiconductor foundry, design firm, or electronics materials supplier
- Fluency in a second language relevant to global hubs (e.g., Japanese, Korean, German)
- Understanding of crystallography, materials properties, and advanced machine learning models for property prediction
Benefits
- A competitive salary
- Equity in CuspAI
- Time off to stay
Skills and tech stack
Questions candidates ask
What is the expected work arrangement?
There is an expectation of being in the office three days per week. Additionally, there may be regular travel required for collaboration and project work.
What is the compensation structure?
The company offers a competitive salary, and candidates will receive equity in CuspAI as a stake in the company's success.
What is the core technical focus of the role?
The main focus initially is partnering closely with the Foundry Partnerships Manager to engage technology and industry partners, translating complex materials science requirements into actionable platform solutions.
What is the required technical background?
Candidates must have a strong academic background in fields like Materials Science, Chemistry, Physics, or Electrical Engineering, along with hands-on experience in computational chemistry tools or Python-based data science workflows.