Key skills
Machine learning systemsDistributed systemsSoftware engineeringPythonProduction software developmentScalable data pipelinesCloud platformsContainersCI/CDDistributed data processingModel deploymentInference servicesDistributed ML frameworksTechnical judgmentMachine LearningMLPyTorchSparkAirflowAWSAzureGCP
About this role
Autodesk is a company that creates software tools for making buildings, machines, and movies, influencing creative problem-solving worldwide. They are seeking a Senior ML Engineer to design and scale systems for machine learning across research and product development, focusing on building infrastructure for data pipelines and production ML workflows.
Responsibilities:
- 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
Requirements:
- 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
- Experience building data pipelines for large-scale structured and semi-structured technical datasets
- Experience with data lineage, provenance, governance, and responsible data usage in ML systems
- Experience with distributed data processing and orchestration systems such as Ray, Airflow, Spark, or similar platforms
- Experience with model deployment, inference services, monitoring, and observability for production ML systems
- Experience building ML-ready representations for geometry, graph, hierarchical, or multimodal data
- Experience with distributed ML frameworks such as PyTorch, Lightning, DeepSpeed, FSDP, Megatron, or similar
- Familiarity with AEC workflows, design data, BIM/CAD formats, or Autodesk products