Autodesk
Technology
MachineLearningEngineer,MLSystemsandInfrastructure
Neural analysis suggests this role is
optimal for Mid+ candidates.
“Machine Learning Engineer, ML Systems and Infrastructure at Autodesk. Skills: ML Systems, Infrastructure, Software Engineering, Data Engineering. Build ML pipeline components. Maintain ML pipeline components”
Industry & Context.
Troubleshoot data issues; Troubleshoot infrastructure issues; Troubleshoot performance issues
What They're Looking For.
Must Have
Bachelor's or Master's degree, Equivalent industry experience, 2 years of industry experience, Software engineering fundamentals, Coding, testing, debugging, Code quality, Proficiency in Python, Building production-quality software, Experience with cloud platforms, Familiarity with containers, Familiarity with version control, Familiarity with CI/CD, Familiarity with modern development workflows, Experience with data-intensive systems, Experience with backend systems, Experience with ML pipelines, Ability to work independently
Nice to Have
Experience building data pipelines, Familiarity with data lineage, Familiarity with data provenance, Familiarity with data governance, Familiarity with responsible data usage, Familiarity with distributed data processing, Familiarity with orchestration systems, Familiarity with model deployment, Familiarity with inference services, Familiarity with monitoring, Familiarity with observability, Familiarity with ML-ready representations, Experience with CAD data formats, Experience with BIM data formats, Experience with AEC data formats, Experience with complex domain-specific data
What You'll Do.
Build ML pipeline components
Maintain ML pipeline components
Develop reliable software
Develop infrastructure
Support scalable ML workflows
Contribute to distributed data processing
Contribute to training systems
Support data ingestion
Support data transformation
Support data validation
Improve observability
Improve operational reliability
Troubleshoot data issues
Troubleshoot infrastructure issues
Troubleshoot performance issues
Participate in design discussions
Document technical decisions
Document operational processes
How You'll Work.
Team & Collaboration
Partner with AI researchers; Partner with software engineers; Partner with platform teams; Collaborate with senior engineers; Collaborate across functions
Full Job Description
**Job Requisition ID #** 26WD98119 **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 solveproblems 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 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. **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 proce
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