Senior Infrastructure Engineer, Dyno Therapeutics

• 5+ years of progressive experience in software engineering
• Experience working with cloud infrastructure (GCP, AWS, or Azure) and comfort with IaC tools like Terraform
• Motivated by solving foundational systems challenges that unblock higher-level innovation in ML and computational science
• You value collaboration, reliability, and craftsmanship in engineering
• A proactive, problem-solving mindset - you don’t just identify challenges; you find solutions
• What You’ll Give:
• Bring an unstoppable work ethic, stepping up when things get tough and adapting as priorities shift
• Embrace challenges as opportunities, finding solutions where none exist and driving innovation forward
• Operate with urgency, responsibility, and resilience, because this mission demands the best from us
• If you’re ready to push boundaries, build the future, and thrive in a fast-moving, high-impact environment—we’d love to hear from you

Desirable

• We are seeking a Platform Infrastructure Sr. Engineer to build the foundational cloud infrastructure powering Dyno’s integrated platform for AI-driven protein design
• As a Platform Infrastructure Engineer, you’ll work alongside senior engineers to design, implement, and scale reliable infrastructure for machine learning and Python services
• As part of the Computational Platform team, you will help design, deploy, and maintain the core systems that make large-scale model training and serving possible
• You’ll contribute directly to infrastructure improvements that increase platform reliability, developer velocity, and the performance of ML workloads across the company
• Build and maintain cloud infrastructure to support model training, data processing, and service deployments using Kubernetes and managed cloud resources
• Develop Infrastructure-as-Code (IaC) to manage environments, networking, and service configuration throughout the code and service lifecycle
• Support major infrastructure initiatives, including:
• Modernizing our application ecosystem by transitioning services to managed cloud services
• Enabling robust multi-node ML training by managing kubernetes cluster deployments
• Contribute to automation, monitoring, and CI/CD systems that improve reliability and developer efficiency
• Collaborate with scientists and engineers across ML and biology