Senior Machine Learning Engineer, MLOps West Coast

Autodesk is a leading company that creates software tools for various industries, including architecture and film. They are seeking a Senior Machine Learning Engineer focused on MLOps to ensure AI-powered experiences meet high standards and to build the infrastructure that supports production models across their products.

Responsibilities:

• Automate model testing and validation. Implement and operate CI/CD pipelines to enable safe, repeatable deployments and rollbacks
• Provision and manage backend resources for inference (compute, containers, scaling), and tune performance, reliability, and cost in production
• Define and continuously monitor health and performance metrics for deployed services. Triage issues by severity and drive timely resolution, including incident response and runbooks
• Own end-to-end REST API integration, connecting backend model services to product and platform surfaces through scalable, containerized services
• Work with researchers, evaluation engineers, product managers, and partner engineering teams to deliver production-ready solutions, communicate status and risks, and escalate when needed

Requirements:

• BS or MS in Computer Science, Computer Engineering, or equivalent industry experience
• 3+ years of professional software engineering experience building and operating production services
• Experience automating testing and deployments using CI/CD, including release workflows that support safe rollouts and rollbacks
• Experience building and operating cloud hosted, containerized services (for example Docker and Kubernetes or similar), including provisioning resources and scaling inference workloads
• Experience building REST APIs using Python based frameworks (or similar), and integrating backend services with product or platform consumers
• Strong software engineering fundamentals: version control, code quality, and writing maintainable, testable software
• Strong written communication skills to document architectures, runbooks, and operational processes
• Experience running production ML or LLM inference services, including performance tuning, cost optimization, and capacity planning
• Experience with observability tooling and practices (metrics, logging, tracing, alerting) and incident response in an on-call environment
• Experience deploying services within an enterprise internal platform environment with standardized pipelines, security controls, and compliance requirements
• Familiarity with rate limiting, authentication and authorization, and API security best practices
• Familiarity with design, manufacturing, or AEC workflows, and how backend services integrate into CAD/BIM product experiences
• Familiarity with Agile or Scrum ways of working