Who you are

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent industry experience
• At least 2 years of industry experience in software engineering, machine learning infrastructure, distributed systems, data platforms, or related areas
• Strong software engineering fundamentals, including coding, testing, debugging, and code quality
• Proficiency in Python and experience building production-quality software
• Experience with cloud platforms such as AWS, Azure, or GCP
• Familiarity with containers, version control, CI/CD, and modern development workflows
• Experience working with data-intensive systems, backend systems, or ML pipelines
• Ability to work independently on well-defined problems with moderate ambiguity
• Experience building data pipelines for large-scale structured and semi-structured technical datasets
• Familiarity with data lineage, provenance, governance, and responsible data usage in ML systems
• Familiarity with distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platforms
• Familiarity with model deployment, inference services, monitoring, and observability for production ML systems
• Familiarity with ML-ready representations for geometry, graph, hierarchical, or multimodal data
• Experience working with CAD, BIM, AEC, or other complex domain-specific data formats
• Is a strong software engineer with interest in machine learning systems
• Enjoys improving reliability, automation, and operational excellence
• Communicates clearly and collaborates well across functions
• Learns quickly and thrives in a fast-moving environment
• Brings sound judgment, curiosity, and ownership to engineering work

What the job involves

• Autodesk is looking for an ML Engineer, ML Systems and Infrastructure to help build the technical foundation behind large-scale machine learning systems
• In this role, you will partner with AI researchers, software engineers, and platform teams tobuild scalable pipelines, training infrastructure, data workflows, and production-ready ML systems that support the nextgeneration of AI-powered product experiences
• This is an engineering-first role focused on building and operating ML systems at scale
• You will work on problems such asdistributed training workflows, data processing pipelines, model evaluation infrastructure, deployment systems, and platform tooling that improves reliability, efficiency, and developer velocity
• This role is fully remote-friendly, with team members distributed across the US and Canada
• Build and maintain components of ML pipelines for data preparation, model training, evaluation, deployment, and monitoring
• Develop reliable software and infrastructure that supports scalable machine learning workflows
• Contribute to distributed data processing and training systems used by researchers and engineering teams
• Support data ingestion, transformation, validation, and serving for large-scale structured and semi-structured technical datasets
• Improve automation, testing, CI/CD, observability, and operational reliability for ML systems
• Troubleshoot data, infrastructure, and performance issues in collaboration with senior engineers
• Participate in design discussions and contribute ideas that improve system scalability, maintainability, and efficiency
• Document technical decisions, workflows, and operational processes clearly