Job Description

Want to build systems that actually hold up under long-running AI workloads?

Most agentic systems for science don’t fail at the model layer. They fail because the infrastructure can’t support long-horizon execution.

You’ll join a team building autonomous AI agents that run full research cycles. Ingesting thousands of papers, forming hypotheses, running experiments, and producing traceable outputs used by real scientific teams.

The challenge is making that work in production.

You’ll own the systems behind it. APIs, data pipelines, and platform architecture designed for long-running workloads, large-scale ingestion, and iterative experimentation loops. This is full-stack in scope, but backend in depth, where system design decisions directly impact what the platform can do.

You’ll be working across:

• Backend services in Python or Node, building scalable APIs (FastAPI/REST)
• Data pipelines supporting agent execution and scientific workflows
• Cloud infrastructure (AWS/GCP), containerisation (Docker, Kubernetes)
• CI/CD, observability, and reliability for systems under continuous load

This isn’t a generalist full-stack role. You’ll need to understand how systems behave under heavy data and compute demands, and be comfortable making architectural trade-offs across distributed systems.

The team is small, high-calibre, and already running real workloads with revenue traction. Backed by $70M+, they’re building infrastructure that defines how AI is applied to scientific discovery.

Salary: $200,000–$350,000 + equity
Location: San Francisco (onsite)