Position Overview The work we do at Autodesk touches nearly every person on the planet. By creating software tools for making buildings, machines, and even the latest movies, we influence and empower some of the most creative people in the world to solve problems that matter. 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 to build scalable pipelines, training infrastructure, data workflows, and production‑ready ML systems that support the next generation 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 as distributed 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. Responsibilities 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 Minimum Qualifications 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 Preferred Qualifications 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 The Ideal Candidate 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 #J-18808-Ljbffr