Autodesk seeks a Senior Machine Learning Engineer focused on MLOps to build and operate infrastructure for AI-powered CAD/BIM features. The role involves automating model deployment, managing inference services, monitoring performance, and collaborating with research teams. The position is based on the US West Coast with flexible work arrangements.
Responsibilities
Test and Deploy Production Models: Automate model testing and validation. Implement and operate CI/CD pipelines to enable safe, repeatable deployments and rollbacks.
Operate Inference Services: Provision and manage backend resources for inference (compute, containers, scaling), and tune performance, reliability, and cost in production.
Monitor Model Health and Performance: Define and continuously monitor health and performance metrics for deployed services. Triage issues by severity and drive timely resolution, including incident response and runbooks.
REST API Integration: Own end-to-end REST API integration, connecting backend model services to product and platform surfaces through scalable, containerized services.
Product Ownership and Cross-functional Collaboration: 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 (e.g., Docker and Kubernetes), including provisioning resources and scaling inference workloads.
Experience building REST APIs using Python based frameworks, 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.
Nice to Have
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.
