Jobs at CuspAI

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.

Lead the technical strategy and project execution for CuspAI in Singapore, managing local teams and translating commercial needs into technical solutions. Drive growth and partnerships while ensuring alignment with global strategies.

Own product requirements, roadmap, and onboarding for partner laboratories integrating experimental workflows, instruments, data systems, and closed-loop campaigns into the CuspAI platform. Translate scientific needs into engineering requirements, validate lab implementations, define data provenance and quality standards, collaborate with ML researchers on optimization campaigns, and establish reusable onboarding patterns and success metrics. The role involves close collaboration with materials scientists, engineers, external labs, and regular travel.

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 CuspAI’s US industrial partnerships pipeline, targeting materials, chemicals, semiconductor, and energy R&D organizations. Source, negotiate, and close complex B2B agreements; translate materials challenges into proposals with scientific and technical teams; expand partner relationships; shape go-to-market priorities and sales processes; provide market feedback to product and leadership; and represent the company at industry events. Regular travel within the US and to Europe is expected.

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.

Build and lead scientific developer relations for an AI materials platform. Engage technical partner organizations, advise computational chemists and materials scientists, run technical workshops and demonstrations, create documentation, notebooks, APIs, and reference materials, and relay partner feedback to AI research, chemistry, and product teams. Collaborate with marketing on technical case studies and content. The role requires strong materials science or related expertise, computational chemistry or Python experience, technical communication skills, and regular travel.

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.