Machine Learning Engineering Manager, Model Delivery
Autodesk · San Francisco, CA
About this role
Autodesk is hiring a manager-level Engineering Manager in the software engineering function based in San Francisco, CA. The posting calls out experience with AWS, GCP, Azure, LLMs. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $148,500–$266,200 per year.
- Role
- Engineering Manager
- Function
- software engineering
- Level
- manager
- Track
- hybrid
- Employment
- Full-time
- Location
- San Francisco, CA
- Education
- Bachelor's degree
- 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 Machine Learning Engineering Manager on the Model Delivery team within Autodesk Research, you will lead production ML engineering across deployment, monitoring, evaluation, reliability, and operational excellence for ML-powered features. This is a hands-on leadership role, combining people leadership with hands-on technical work. You’ll help deliver and operate ML systems that include 2D/3D generative models and other AI capabilities used across Autodesk products.
You will report to the Head of Model Delivery within Autodesk Research. This role is based in proximity to our North American Autodesk offices, including San Francisco, Portland, Boston, Toronto, Montreal, and Vancouver. We support both in-person, hybrid, and remote work.
Responsibilities
Lead and grow a team of ML engineers focused on production ML systems
Lead model improvements in response to production issues, product feedback, and new research or platform advancements
Lead production release processes for ML services, including release planning, CI/CD, staged rollouts, and rollback procedures
Build and operate observability and on-call practices for ML features, including monitoring, alerting, dashboards, incident response, and post-incident reviews
This is an excerpt. Read the full job description on Autodesk careers →