Lead Platform Machine Learning Engineer
Skills
About the Role
You will design, build, and maintain the infrastructure that supports machine learning training, deployment, and inference workloads. You'll own and improve CI/CD pipelines, infrastructure-as-code, observability, and operational tooling for ML systems, while operating and evolving production systems with a strong focus on reliability, performance, and cost efficiency. You will build and maintain Kubernetes- and cloud-based services that support model execution and data workflows, and partner closely with data scientists and engineers to support their model deployment and operational needs. You'll also improve developer workflows through automation, tooling, and platform abstractions, and participate in on-call rotations and operational ownership for platform components.
Requirements
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 7+ years of experience in software engineering, platform engineering, or DevOps/SRE roles
- Strong systems engineering background with production operations experience
- Strong experience with backend or platform services
- Hands-on experience with cloud infrastructure and tooling (e.g., AWS, Docker, Kubernetes, Terraform, CI/CD, monitoring)
- Experience operating distributed systems in production
- Experience with machine learning infrastructure, model serving, or data platforms is a strong plus
- Strong problem-solving skills and an operations-first mindset
- Ability to thrive in a fast-paced, high-tech environment and manage complex problems
Responsibilities
- Design, build, and maintain infrastructure supporting ML training, deployment, and inference workloads
- Own and improve CI/CD, infrastructure-as-code, observability, and operational tooling for ML systems
- Operate and evolve production systems with a strong focus on reliability, performance, and cost efficiency
- Build and maintain Kubernetes- and cloud-based services that support model execution and data workflows
- Partner with data scientists and engineers to support model deployment and operational needs
- Improve developer workflows through automation, tooling, and platform abstractions
- Participate in on-call and operational ownership for platform components