Senior Machine Learning Engineer, MLOps West Coast
Autodesk · San Francisco, CA
About this role
Autodesk is hiring a senior-level Machine Learning Engineer based in San Francisco, CA. The posting calls out experience with Python, REST APIs, Kubernetes, Docker. Compensation is listed at $131,400–$235,950 per year.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- senior
- Track
- Individual contributor
- Employment
- Full-time
- Location
- San Francisco, CA
- Posted
- Apr 20, 2026
More roles at Autodesk
Job description
from Autodesk careersJob Requisition ID #
Position Overview
The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world.
As a Senior Machine Learning Engineer focused on Machine Learning Ops (MLOps) for CAD and BIM, you will ensure AI-powered experiences meet high standards for reliability, scalability, and operational excellence across Autodesk products. You will build and operate the infrastructure that takes models from development into production, including deployment automation, monitoring, and secure, scalable service integration. You will partner closely with researchers, evaluation engineers, and product teams to translate evaluation requirements into production quality gates, reduce operational risk, and continuously improve model performance in real customer environments.
You will report to a manager in the Model Delivery team within Autodesk Research. This role is based in proximity to our North American west coast offices, including San Francisco, Portland, and Vancouver. We support both in-person, hybrid, and remote work.
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.