Software Engineer - Infrastructure

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 an Infrastructure Software Engineer at Baseten, you'll build and maintain components of our ML inference platform that powers production AI applications. You'll contribute to the core infrastructure, enabling developers to deploy, scale, and monitor ML models with high performance. 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 - Develop infrastructure components for our ML inference platform using Python and Go - Implement and maintain Kubernetes deployments for model serving - Contribute to our inference orchestration layer for model deployments - Build and enhance monitoring systems for model performance metrics - Implement efficient resource management solutions for ML workloads - Support infrastructure automation to improve ML deployment workflows - Work closely with team members to im

Key Responsibilities

• Develop infrastructure components for our ML inference platform using Python and Go
• Implement and maintain Kubernetes deployments for model serving
• Contribute to our inference orchestration layer for model deployments
• Build and enhance monitoring systems for model performance metrics
• Implement efficient resource management solutions for ML workloads
• Support infrastructure automation to improve ML deployment workflows

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.