Edison Scientific

Build the machine learning models, agents, training systems, and inference infrastructure that power AI-driven scientific discovery. This role combines applied ML research with production engineering across the full ML lifecycle.

Build and evolve production AI scientist agents that help accelerate scientific discovery and medicine development. This hands-on role combines LLM application engineering, agent infrastructure, experimentation, and real-world collaboration with life sciences researchers.

Build and operate the cloud infrastructure that enables Edison Scientific’s AI scientist platform to deploy securely within enterprise customer environments. This hands-on role owns BYOC architecture, automation, reliability, and client deployment support across AWS and GCP.

Own and scale the Kubernetes-based infrastructure powering autonomous AI scientist agents and scientific research workloads. This role focuses on platform reliability, automation, observability, and resource orchestration in a fast-moving research environment.

Lead the platform infrastructure behind autonomous AI scientist agents, designing and operating Kubernetes systems that manage thousands of persistent workloads. This principal-level role owns orchestration, scaling, reliability, and infrastructure architecture for scientific discovery.

Build backend-focused full-stack systems that power AI scientist agents and accelerate scientific discovery. You’ll own features end to end across APIs, data pipelines, databases, and user-facing workflows.

Build high-performance frontend experiences for an AI-powered scientific discovery platform used by researchers. You’ll turn complex AI and scientific workflows into intuitive interfaces, visualizations, and reusable design-system components.

Senior AI full-stack engineer who will build backend-focused platform services, APIs, data pipelines, and user-facing features powering Edison Scientific’s AI scientist. The role offers end-to-end ownership on a platform engineering team working at the intersection of software and scientific discovery.