Machine Learning Engineering Manager, Model Delivery

Autodesk Autodesk · Enterprise · San Francisco, CA +8

Machine Learning Engineering Manager on the Model Delivery team within Autodesk Research, leading production ML engineering across deployment, monitoring, evaluation, reliability, and operational excellence for ML-powered features. This role involves leading and growing a team of ML engineers, improving models based on production issues and feedback, leading production release processes, building observability and on-call practices, developing evaluation frameworks, and leading reliability/performance/cost improvements for inference and serving. The role also partners with researchers, product, and platform teams to define quality bars and production readiness, including Trusted AI requirements, and establishes production standards for ML features.

What you'd actually do

  1. Lead and grow a team of ML engineers focused on production ML systems
  2. Lead model improvements in response to production issues, product feedback, and new research or platform advancements
  3. Lead production release processes for ML services, including release planning, CI/CD, staged rollouts, and rollback procedures
  4. Build and operate observability and on-call practices for ML features, including monitoring, alerting, dashboards, incident response, and post-incident reviews
  5. Develop and maintain scalable evaluation frameworks, datasets, and automated regression tests to prevent quality regressions

Skills

Required

  • BS/MS in CS/Engineering or equivalent experience
  • Experience building and operating software systems, including production ML systems
  • People leadership experience, or strong technical leadership experience (mentoring, setting direction, driving delivery)
  • Experience with cloud infrastructure and production observability (AWS, Azure, or GCP)
  • Experience with CI/CD, reproducible deployments, and operating services in production
  • Strong written communication and documentation skills

Nice to have

  • Owned an end-to-end production ML service (releases, monitoring, incidents, and improvements)
  • Experience with model evaluation, quality metrics, and continuous testing in CI/production
  • Experience optimizing inference latency and cost
  • Experience deploying large or foundation models in production (performance, scaling, and efficiency tradeoffs)
  • Experience with 3D data (geometry/CAD/BIM) and/or generative AI systems is a plus
  • Familiarity with CAD, BIM, manufacturing, AEC, or media workflow

What the JD emphasized

  • production ML systems
  • production release processes
  • production readiness
  • production standards
  • production issues
  • production observability
  • production ML features
  • production ML service
  • production readiness

Other signals

  • production ML systems
  • ML features
  • generative models
  • foundation models