Senior Machine Learning Test Engineer, Autodesk

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience
• 7+ years of professional experience in software engineering or QA for ML/AI systems
• Strong programming skills in Python, with experience in test automation
• Familiarity with popular CAD environments tooling
• Proficient in Automation and UAT test suite/framework
• Experience designing QA frameworks or platforms used by multiple teams
• Excellent problem-solving skills and attention to detail
• Strong communication and collaboration skills
• Understanding of software architecture and design patterns
• Ability to work in an agile development environment
• You demonstrate initiative to provide solutions and to learn and develop new technologies
• Comfortable building QA systems from scratch and writing maintainable automation
• You enjoy learning and collaborating across global locations
• You are comfortable working in newly forming ambiguous areas
• You are comfortable building scalable and maintainable systems that will be relied on by others
• You can communicate well with others

Desirable

• As a Senior Machine Learning QA Engineer in the Research Enablement team, you will work side-by-side with researchers, Machine Learning Engineers and software engineers to define and uphold quality standards for ML systems
• You are a quality-focused engineer who is passionate about reliable, repeatable evaluation of ML models and data
• Your skills span test strategy, automation, and a little MLOps, with a strong software engineering base
• You are excited to collaborate across research and product to ship ML capabilities with clear quality gates
• You are comfortable working at the intersection of research and product and are competent in using Autodesk CAD software
• Reporting Structure: You will report to an Engineering Manager in Research Enablement
• Define ML quality strategy and acceptance criteria across data, model, and system levels
• Design and maintain model evaluation suites, metrics, and test datasets
• Evaluating CAD RL model outputs for geometric validity or policy stability
• Defining structured rubrics that translate qualitative findings into measurable evaluation gates
• Testing ML Models from product side
• API Testing
• Automate ML QA workflows using Python and CI/CD (e.g., GitHub Actions, Jenkins)
• Create and maintain test harnesses for ML services and APIs
• Mentor teams on ML QA best practices and consistent evaluation standards
• Build quality gates for training and deployment pipelines (e.g., regression checks, drift detection)
• Contribute to multi-team projects and codebases, ensuring code quality and consistency
• Participate in code reviews and provide constructive feedback to peers
• Document and present findings and ideas across the company