Jobs at CuspAI

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

Lead technical enablement and scientific advisory for partners adopting CuspAI's platform. Engage computational chemists and materials scientists, run workshops and code-alongs, create advanced developer resources (docs, notebooks, APIs), and drive cross-functional feedback into AI research and product. Support partner discovery sprints and co-author deep-tech content to showcase materials breakthroughs.

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

Lead and execute technical programs bridging AI/ML model development and experimental materials science. Translate scientific goals into deliverables, manage resources, timelines, compute, risks, partner collaborations, and scalable processes to accelerate discovery and customer projects.

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.

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.

Lead and coordinate technical AI/ML programs bridging research and lab implementation. Plan and deliver projects, manage compute/resources/dependencies, liaise with industry and academic partners, drive proposals and pilots, refine scalable processes, track metrics, and support IP, compliance, and publication efforts.

Lead CuspAI's data strategy and team to acquire, generate, curate, and deliver ML-ready materials data. Build partnerships, prioritize data investments, oversee data-generation programs, set quality/provenance standards, and act as single point of accountability for research data commitments.

Lead full-cycle recruitment for technical partnerships, business development, engineering and research roles. Design sourcing strategies, screen PhD-level and technical talent, partner with hiring managers on JD, assessment and calibration, maintain ATS data hygiene and dashboards, drive DEIB in hiring, and manage candidate experience through offer and onboarding while supporting regional growth and international collaboration.