Member of Technical Staff, AI Infrastructure Engineer
- Compensation
- $175K–$240K / yr
- Location
- San Francisco
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 4+ years
- Category
- Devops
- Posted
Skills
Must have
- 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.
- Curiosity, strong problem-solving skills, and a desire to learn new technologies.
Nice to have
- Security like experience with gvisor/kata containers, snapshots
Full description
About Us
Edison Scientific builds and deploys AI scientist agents to accelerate science and the development of new medicines. We are an ambitious team run by scientists and engineers from leading institutions across biology, physics, chemistry, and AI.
About the Role
As a Member of Technical Staff, Infrastructure Engineer, you'll play a key role in designing, scaling, and operating the core platform infrastructure that powers autonomous scientific discovery.
Our mission is to build an AI scientist, and you'll own the infrastructure foundation it runs on. AI agents performing long-running scientific research demand resilient scheduling, lifecycle management, and resource orchestration far beyond typical cloud-native workloads. This role will establish infrastructure best practices, and partner closely with backend engineers, ML engineers, and researchers to deliver a production-grade environment that lets science move faster.
Key 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 our Kubernetes environments.
- Troubleshoot infrastructure and production issues across distributed systems and participate in incident response efforts.
- Collaborate with backend, ML, and research teams to understand workload requirements and implement reliable infrastructure solutions.
- Contribute to infrastructure best practices, documentation, and operational processes.
Required Qualifications
- 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.