Jobs at CuspAI

Own product marketing for CuspAI’s AI Materials Foundry and emerging science products. Lead positioning, messaging architecture, launches, GTM strategy, pricing and packaging, self-serve adoption, competitive intelligence, partner ecosystem marketing, sales enablement, and customer case studies. Translate complex scientific and AI capabilities into credible commercial narratives while collaborating with researchers, product, engineering, commercial, partnerships, communications, and design teams. Build and eventually lead the product marketing function.

Build and operate software integrations connecting CuspAI’s AI platform with partner laboratories. Develop APIs and data models, integrate instruments, robotics, LIMS/ELN systems, and scheduling tools, and ensure reliable data exchange through retries, queues, monitoring, and alerting. Support partner onboarding, deploy containerized cloud services, and collaborate with materials scientists and engineers to create scalable closed-loop discovery workflows.

Build and operate security controls for AI models, research data, cloud infrastructure, and CI/CD pipelines. Responsibilities include defending against data poisoning and prompt injection, implementing AI security posture management, securing Docker and Kubernetes environments, codifying cloud security policies with infrastructure as code, managing IAM and secrets, monitoring for incidents, and promoting security practices across interdisciplinary engineering and research teams.

Build and operate a cloud-native ML infrastructure platform on Kubernetes. Own MLOps tooling, CI/CD, model registries, experiment tracking, distributed GPU training, monitoring, configuration management, cost optimization, and deployment automation. Create developer tools that simplify the path from research experiments to production while collaborating with ML researchers, chemists, materials scientists, and software engineers.

Optimize large-scale AI training and inference workloads across NVIDIA GPU infrastructure. Profile model execution, identify bottlenecks, improve MFU, develop CUDA or Triton kernels, optimize distributed communication, streamline data pipelines, and build observability dashboards and scaling tools. Collaborate with AI researchers and infrastructure engineers to improve model-hardware compatibility and future cluster design. The role is based in European offices with three days per week onsite and regular international travel.

The Data Engineer will build data pipelines for materials science, ensuring data quality and collaborating with ML researchers while managing large datasets.

Build and own a suite of internal applications for AI and materials research workflows. Responsibilities include architecting backend services in Python, developing responsive frontend interfaces, integrating research engines and complex datasets, and managing tools through their full lifecycle. The role partners with product, AI research, data engineering, and platform teams to create scalable, intuitive products and may later support external-facing interfaces.

Own product requirements, roadmap, and onboarding for partner laboratories integrating experimental workflows with the CuspAI platform. Translate scientific workflows, instruments, data systems, and experimental needs into engineering requirements and reusable integration patterns. Collaborate with materials scientists, ML researchers, and engineers on closed-loop optimization campaigns, validation benchmarks, and success metrics. Serve as the scientific product counterpart for partner labs, with regular collaboration, workshops, conferences, and travel.

Build and manage a B2B partnerships pipeline with industrial R&D, materials, chemicals, and semiconductor organizations. Source and close complex, multi-stakeholder deals; translate materials challenges into proposals with scientists and technical program managers; negotiate agreements; expand customer relationships; shape go-to-market strategy, sales processes, and partnership playbooks; and represent the company at industry events. Regular travel across the US and Europe is required.

Build and expand CuspAI’s global laboratory partner ecosystem for AI-driven materials discovery. Develop lab collaboration frameworks, identify new partners, coordinate cross-functional timelines, support automated experimental workflows, and connect partner feedback to platform development. The role requires hands-on materials science or chemistry experience, laboratory automation expertise, experimental campaign development, and communication across scientific, technical, and experimental teams. Regular travel to partner sites and collaboration locations is expected.

Own and optimize recruitment operations for a rapidly growing AI company. Responsibilities include administering Ashby, maintaining ATS data integrity, building recruiting dashboards, managing vendor relationships, creating standardized hiring processes, coordinating complex global interviews, supporting candidate logistics, facilitating interviewer training, and improving candidate onboarding handoffs. The role partners with Talent Partners, Sourcers, hiring managers, People Operations, and IT to streamline technical, scientific, operational, and commercial hiring.

Develop intelligent agents for a materials discovery engine, orchestrating scientific workflows and integrating experimental design for sustainability solutions.

3-month engineering internship to distill fast, accurate ML force fields for Monte Carlo (GCMC), integrate them into kUPS, validate against classical force fields, profile and optimize inference in the MC inner loop, curate datasets, and collaborate on benchmarks and publications.