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Edison Scientific

AI platform for scientific R&D workflows

AI Infrastructure Engineer

Platform EngineeringConfirmed live in the last 24 hours
Full-time$175k - $240k /yrIn-person

About the Job

Requirements

  • At least 4 years of software, infrastructure, platform, or DevOps engineering experience.
  • Experience working with Kubernetes in development or production environments.
  • Familiarity with cloud platforms such as AWS, GCP, or Azure.
  • Proficiency in at least one programming language such as Python, Go, Java, or TypeScript.
  • Experience with infrastructure-as-code tools such as Terraform, Pulumi, or similar technologies.
  • Understanding of containerized applications, networking fundamentals, and distributed systems concepts.
  • Experience using CI/CD pipelines, version control systems, and automated testing practices.
  • Ability to work independently while collaborating effectively across teams.

Responsibilities

  • Build, maintain, and improve Kubernetes-based infrastructure that supports agent workloads, research services, and internal platforms.
  • Contribute to cluster scaling, resource management, and operational improvements as platform usage grows.
  • Develop and maintain infrastructure tooling, automation, and deployment workflows that improve reliability and developer productivity.
  • Help implement monitoring, observability, and alerting systems to ensure platform health and performance.
  • Support storage, networking, and security initiatives within Kubernetes environments.
  • Troubleshoot infrastructure and production issues across distributed systems and participate in incident response efforts.
  • Collaborate with backend, machine learning, and research teams to understand workload requirements and implement reliable infrastructure solutions.
  • Contribute to infrastructure best practices, documentation, and operational processes.

Desired Qualifications

  • Security-related experience with gVisor or Kata Containers, and snapshots.

About the Company

Edison Scientific

AI platform for scientific R&D workflows

Edison Scientific provides an AI-powered platform that unifies research tasks in one environment. It combines literature synthesis, data analysis, molecular design, and the creation of publication-ready figures and reports so researchers can move from hypothesis to validated results without switching between many tools. The platform uses natural language processing and machine learning to digest scientific literature and data, automate workflows, and run tasks in parallel to speed up discovery. Compared with competitors, Edison Scientific stands out by offering an integrated, end-to-end R&D workflow that links literature, data, and design in a single platform, reducing manual handoffs and enabling scalable research. The company’s goal is to streamline R&D processes, shorten the time to insight, and help organizations rapidly validate findings and publish their work.

Company size
51-200
Company stage
Seed
Total funding
$70M
Headquarters
San Francisco, CA
Founded
2025

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