Cloud Platform Engineer

About the Role

ABOUT BASETEN Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products. THE ROLE As a Cloud Platform Engineer, you'll envision and build robust systems and processes that ensure our infrastructure is scalable, reliable, and efficient. This can range from automating deployments and monitoring systems to optimizing performance and managing incidents. We all work closely with our users, learning from their past struggles in operationalizing ML, onboarding them onto our platform, and turning our learnings into ideas for improving Baseten. EXAMPLE INITIATIVES You'll get to work on these types of projects as part of our Infrastructure team: - Multi-cloud capacity management https://www.baseten.co/blog/how-baseten-multi-cloud-capacity-management-mcm-powers-cloud-self-hosted-and-hybr/ - Inference on B200 GPUs https://www.baseten.co/blog/accelerating-inference-nvidia-b200-gpus/ - Multi-node inference https://www.baseten.co/blog/how-multi-node-inference-works-llms-deepseek-r1/ - Fractional H100 GPUs for efficient model serving https://www.baseten.co/blog/using-fractional-h100-gpus-for-efficient-model-serving/ RESPONSIBILITIES - Build and maintain scalable infrastructure to support the deployment and operation of machine learning models. - Establish standards and best practices for reliability and performance across the infrastructure. - Automate processes when relevant, particularly for managing CI/CD pipelines. - Own products

Key Responsibilities

• Build and maintain scalable infrastructure to support the deployment and operation of machine learning models.
• Establish standards and best practices for reliability and performance across the infrastructure.
• Automate processes when relevant, particularly for managing CI/CD pipelines.
• Own products

Requirements & Qualifications

• Strong problem solving foundations, knowledge of modern engineering practices, and interest in technology systems.

Perks & Benefits

• Official mentor support
• verified hiring timeline
• and career progression.