Machine Learning Engineering Manager, Model Delivery

Autodesk

Hybrid

Quick summary

Work type
Hybrid
Location
San Francisco, CA · Massachusetts · Boston, MA · Portland, OR · Vancouver, British Columbia, Canada
Salary
$148,500–$266,200 / yr
Posted
98 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $223k
This role $207k
$134k most similar roles pay here $280k

This role pays less than 61% of similar roles. Most pay $187,464–$259,212 — the shaded band above. At the midpoint, this role pays about $207k versus about $223k for comparable roles.

Based on 240 similar postings.

Employer

About Autodesk

Autodesk is a global leader in 3D design, engineering, and entertainment software, enabling users to imagine, design, and create a better world.

Autodesk currently has 44 open roles on FindRole.

Listed pay typically runs $139,000–$249,260 across 42 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineering Manager, Model Delivery

As a Machine Learning Engineering Manager on the Model Delivery team within Autodesk Research, you will lead a team of ML engineers in developing and maintaining production-level machine learning systems that power 2D/3D generative models and other AI capabilities across various Autodesk products. Your day-to-day responsibilities include overseeing model improvements, release processes, observability practices, and reliability enhancements while ensuring compliance with Trusted AI requirements. You will work closely with researchers, product teams, and platform engineers to integrate ML features into products and establish robust production standards. The role requires expertise in cloud infrastructure, CI/CD pipelines, and experience with large-scale model deployment, as well as a background in 3D data and generative AI systems being advantageous.

What you'll do

  • Lead and grow a team of ML engineers focused on production systems.
  • Improve models in response to issues, feedback, and new advancements.
  • Manage release processes for ML services, including planning and rollouts.
  • Build observability practices for monitoring, alerting, and incident response.
  • Develop scalable evaluation frameworks to prevent quality regressions.
  • Enhance reliability and performance of inference and serving systems.
  • Establish production standards and governance across ML features.

What we're looking for

  • BS/MS in CS/Engineering or equivalent experience leading technical teams
  • Experience building and operating production ML systems
  • Leadership in people management or strong technical leadership skills
  • Proficiency with cloud infrastructure and production observability tools
  • Expertise in CI/CD, reproducible deployments, and service operations
  • Strong written communication and documentation abilities
  • Ownership of end-to-end production ML services lifecycle

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