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.ResponsibilitiesBuild and maintain components of ML pipelines for data preparation, model training, evaluation, deployment, and monitoringDevelop reliable software and infrastructure that supports scalable machine learning workflowsContribute to distributed data processing and training systems used by researchers and engineering teamsSupport data ingestion, transformation, validation, and serving for large‑scale structured and semi‑structured technical datasetsImprove automation, testing, CI/CD, observability, and operational reliability for ML systemsTroubleshoot data, infrastructure, and performance issues in collaboration with senior engineersParticipate in design discussions and contribute ideas that improve system scalability, maintainability, and efficiencyDocument technical decisions, workflows, and operational processes clearlyMinimum QualificationsBachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent industry experienceAt least 2 years of industry experience in software engineering, machine learning infrastructure, distributed systems, data platforms, or related areasStrong software engineering fundamentals, including coding, testing, debugging, and code qualityProficiency in Python and experience building production‑quality softwareExperience with cloud platforms such as AWS, Azure, or GCPFamiliarity with containers, version control, CI/CD, and modern development workflowsExperience working with data‑intensive systems, backend systems, or ML pipelinesAbility to work independently on well‑defined problems with moderate ambiguityPreferred QualificationsExperience building data pipelines for large‑scale structured and semi‑structured technical datasetsFamiliarity with data lineage, provenance, governance, and responsible data usage in ML systemsFamiliarity with distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platformsFamiliarity with model deployment, inference services, monitoring, and observability for production ML systemsFamiliarity with ML‑ready representations for geometry, graph, hierarchical, or multimodal dataExperience working with CAD, BIM, AEC, or other complex domain‑specific data formatsThe Ideal CandidateIs a strong software engineer with interest in machine learning systemsEnjoys improving reliability, automation, and operational excellenceCommunicates clearly and collaborates well across functionsLearns quickly and thrives in a fast‑moving environmentBrings sound judgment, curiosity, and ownership to engineering work#J-18808-Ljbffr