Senior Machine Engineer, Autodesk

• This role is ideal for an engineer who is deeply interested in scalable systems and production-grade ML infrastructure
• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field, or equivalent industry experience
• At least 3 to 4 years of industry experience building and operating production software, ML systems, distributed infrastructure, or large-scale data pipelines
• Strong experience in software engineering, distributed systems, backend systems, or ML infrastructure
• Strong proficiency in Python and experience delivering production-quality systems
• Experience designing and operating scalable data or compute pipelines
• Experience with cloud platforms such as AWS, Azure, or GCP
• Familiarity with containers, CI/CD, observability, and release quality practices
• Ability to independently drive technical execution on complex work with limited oversight
• Thinks like a systems engineer and executes like a strong software developer
• Can balance short-term delivery with long-term platform health
• Brings strong technical judgment and ownership
• Improves team effectiveness through mentoring and engineering rigor
• Enjoys solving scaling, performance, and reliability challenges

Desirable

• Autodesk is seeking a Senior ML Engineer, ML Systems and Infrastructure to design and scale the systems that enable machine learning across research and product development
• You will help build the infrastructure behind large-scale data pipelines, distributed training systems, evaluation frameworks, and production ML workflows that support foundation models and ML-powered product features
• You will operate independently across multiple parts of the stack and help define strong engineering practices for reliability, performance, and maintainability
• Design and build scalable systems for ML training, evaluation, deployment, and monitoring
• Develop and improve data pipelines that process large-scale structured and semi-structured technical datasets
• Optimize distributed workflows for performance, reliability, resource utilization, and cost efficiency
• Build platform capabilities such as experiment tracking, model versioning, checkpointing, reproducibility, and observability
• Contribute to model deployment, inference services, and production monitoring workflows
• Improve data quality, lineage, provenance, and operational transparency across ML pipelines
• Contribute to architecture and design discussions across the team
• Identify and resolve bottlenecks in data, compute, orchestration, and observability layers
• Mentor engineers through code reviews, design guidance, and knowledge sharing
• Collaborate closely with researchers, product engineers, and platform partners to turn ML workflows into robust engineering systems