Senior Machine Learning Test Engineer

Autodesk is a global leader in design software, and they are seeking a Senior Machine Learning Test Engineer to work in their Research Enablement team. The role involves defining quality standards for ML systems, collaborating with various teams, and ensuring reliable evaluation of ML models and data.

Responsibilities:

• 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

Requirements:

• 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
• Experience with data validation tooling (e.g., Great Expectations) or labeling workflows
• Familiarity with ML frameworks (e.g., PyTorch, TensorFlow)
• Experience with CI/CD tools and processes
• Experience with data pipelines and orchestration tools (e.g., Airflow, Metaflow)
• Familiarity with MLOps practices (model monitoring, drift, deployment checks)
• Experience with ML evaluation methods, metrics, and benchmarking
• Passion for learning new technologies and improving existing systems
• Experience with cloud providers (e.g., AWS, Azure, Google Cloud Platform)
• Experience testing ML services in production environments
• Knowledge of experiment tracking tools (e.g., Comet, MLflow, Weights & Biases)