Cloud Platform Engineer

Role overview Design and operate the infrastructure that supports deploying and serving machine learning models. You’ll improve scalability, reliability, and performance while partnering with users and cross-functional teams to turn operational needs into technical projects. Example areas include multi-cloud capacity, large GPU workloads, and distributed inference.

Responsibilities - Build and maintain infrastructure for deploying and operating machine learning models. - Set reliability and performance standards across the platform. - Automate operational workflows, including continuous integration and delivery. - Lead projects from defining the problem and requirements through implementation and delivery. - Work with partner teams to translate their needs into practical technical solutions. - Mentor junior colleagues and share knowledge across the organization.

Requirements - Degree in computer science, engineering, mathematics, or a related field. - Extensive hands-on experience with Kubernetes. - Experience building and maintaining scalable infrastructure. - Experience with infrastructure-as-code tools such as Terraform, CloudFormation, or Pulumi. - Experience with CI/CD systems such as GitHub Actions, GitLab CI, CircleCI, or Jenkins. - Ability to own projects from specification through execution and openness to learning about machine learning.

Nice to have - Experience with open-source observability tools such as Prometheus, ELK, Grafana, or OpenTelemetry. - Familiarity with infrastructure for multi-cloud capacity management, GPU-based inference, or multi-node model serving.

Benefits and work setup Compensation includes equity. Benefits include flexible paid time off with a year-end company break, paid parental leave, and a family-building stipend. In the U.S., medical, dental, and vision coverage for employees and dependents is fully covered, and a 401(k) is available. The work also offers exposure to a range of machine learning startups.