Teams at Schrödinger, Inc.

Own the infrastructure powering machine learning and AI agent platforms across company and customer cloud environments. Build GitOps-driven deployments, secure multi-tenant Kubernetes sandboxes, hardened CI/CD pipelines, restricted-egress partner environments, and reliable GPU training and inference systems. Collaborate with ML engineers and scientists while implementing observability, SLOs, cost controls, and on-call operations across GCP and AWS.

Manage AI and machine learning products for materials science, including customer demonstrations, roadmap planning, product expansion, ML model development, and leadership of the materials science ML team. The role requires deep expertise in materials science and ML, software-industry experience, customer engagement, public presentations, and global travel.

Develop and automate infrastructure systems that build, test, deploy, and monitor development and production environments. Design secure internal infrastructure services using Terraform, GitHub Actions, Python, and Bash. Troubleshoot infrastructure issues, manage containerized web services, support Linux systems administration, and implement robust deployment automation and continuous integration solutions.