Scientific Software Engineer (Lab Integration)

CuspAI is the frontier AI company on a mission to solve the breakthrough materials needed to power human progress. While nature took billions of years to perfect molecules, we are harnessing AI to unlock trillion-dollar materials breakthroughs in months, not millennia. Our founding team is the most cited in the world, comprised of world-class researchers in AI, chemistry and engineering.

We are working on some of the hardest and most important challenges including energy, clean water, the future of compute, and carbon capture, and this is just the start of what our 'search engine' for next-generation materials will unlock.

We invite you to be part of a diverse, innovative team at the intersection of AI and materials science, working to create impactful partnerships that drive innovation, scalability, and industry collaboration. This work matters. Your work matters.

We’re on the cusp of the on-demand materials era. Join us.

We are seeking a Scientific Software Engineer to build and run the systems that connect our AI platform to physical laboratories around the world. Together with the team, you will shape the software layer of our closed-loop discovery workflow: our agents propose experiments, partner labs execute them, and the results feed back into our models to guide the next set of experiments.

This role sits between lab automation and production engineering. You will bring hands-on experience with scientific or lab automation, and the engineering discipline to make those integrations secure, reliable and scalable across many partners.

You will be part of a small team of materials scientists, ML engineers and software engineers, working closely with each other and with our partner labs. You will help define how our AI platform connects to the physical world and shape the tools and practices that link external partner labs to our platform. You will thrive here if you enjoy collaborating across disciplines and learning from people whose expertise differs from your own.

You will turn experimental labs into first-class, programmable parts of the CuspAI platform. Every partner you onboard, and every integration you harden, shortens the loop between an AI-generated hypothesis and a validated material.

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  Help shape how we connect to partner labs around the world, accounting for different levels of automation and AI-readiness, then design, build and operate the services that send experiment requests from our AI agents and reliably bring results back.

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  Build APIs and data models (e.g. FastAPI, Pydantic) for bidirectional lab-to-cloud data exchange.

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  Integrate with lab instruments, robotic platforms, LIMS/ELN systems and scheduling software, translating between disparate formats and CuspAI's data platform.

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  Make integrations reliable at scale: idempotency, retries, queueing, monitoring and alerting across many partners and time zones.

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  Assist with the technical onboarding of new lab partners, assessing their systems and setting up integrations quickly and repeatedly.

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  Collaborate day to day with experimental materials scientists, optimisation experts and engineers on the team, co-designing workflows and feeding lab realities back into platform design.

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  Deploy and run containerised services on cloud infrastructure, with testing and CI/CD as standard.

• Hands-on experience with lab automation, self-driving labs or other scientific discovery workflows, e.g. instrument control, robotic platforms, or high-throughput experimentation data pipelines.

• Strong software engineering background, with production experience building and operating backend services.

• Deep fluency in Python, including typed, well-tested code and API design.

• Experience designing distributed systems that integrate with external parties: APIs, message queues or event-driven architectures, and data contracts.

• Solid understanding of security practices for externally facing systems.

• A collaborative mindset: you enjoy working in a small, multidisciplinary team, and can communicate clearly with experimental scientists, optimisation experts and engineers alike.

• Experimental lab experience in materials science, chemistry or chemical engineering.

• Experience with workflow orchestration (e.g. Dagster, Flyte, Prefect) or active learning loops.

• Experience with infrastructure as code (e.g. Terraform) and observability tooling.

• Security or compliance experience or handling partner IP and confidential data.

• Experience working across distributed teams and multiple time zones.