1 day ago
Toronto, CanadaSenior
Responsibilities
- Design, develop, test, deploy, and maintain production-grade machine learning pipelines and reusable ML services.
- Build model evaluation frameworks and automated evaluation pipelines using metrics, curated datasets, regression testing, model-based evaluation, and human evaluation where appropriate.
- Develop distributed data and ML processing pipelines for documents, web content, structured data, and unstructured data at high volume.
- Design cloud orchestration and resilient processing patterns including concurrency management, queues, checkpointing, retries, failure recovery, rate limiting, and idempotent processing.
- Optimize AI/ML systems for latency, throughput, scalability, infrastructure utilization, and inference cost.
- Implement MLOps and LLMOps practices including versioning, experiment tracking, deployment automation, monitoring, rollback, and lifecycle management.
- Build observability and monitoring for pipeline health, model performance, data quality, failures, cost, model drift, and data drift.
- Partner with platform, data engineering, data science, product, and business teams to translate business needs into production ML architectures.
- Contribute to engineering standards, reference architectures, design reviews, code reviews, technical documentation, and AI/ML best practices.
Requirements
- Bachelor’s degree in Computer Science, Engineering, Machine Learning, Data Science, Information Systems, or a related technical discipline.
- At least 5 years of machine learning engineering, data engineering, or related experience, including significant production-system development.
- Demonstrated experience designing and operating production ML systems rather than only notebook-based experimentation.
- Strong Python programming skills for modular, testable, maintainable, production-quality software.
- Hands-on experience with Snowflake, Snow SQL, Snow Pipe, and Snowflake cost optimization.
- Experience with Airflow or comparable workflow orchestration frameworks.
- Experience with DBT and with building and deploying machine learning inference pipelines and services.
- Experience designing distributed data or ML processing pipelines for high-volume workloads.
- Experience deploying workloads to a major cloud environment, preferably AWS, including compute, storage, event processing, monitoring, and distributed execution services.
- Experience with Git-based development workflows, code reviews, branching strategies, and collaborative engineering practices.
- Familiarity with MLOps concepts including experiment tracking, model lifecycle management, deployment, monitoring, reproducibility, and versioning.
- Experience with structured and unstructured data and preprocessing, enrichment, and transformation pipelines.
- Strong analytical, debugging, problem-solving, written communication, verbal communication, and cross-functional collaboration skills.
- Ability to work with geographically distributed teams across multiple time zones and familiarity with Agile/Scrum practices.
Benefits
- For Canada-based roles, the stated starting base salary range is $123,000 to $180,400 CAD, with offers based on experience and geographic location.
- The compensation package may include annual cash bonuses, stock grants, and comprehensive benefits.
- The role may require in-person onboarding and/or in-person identity verification.
Categories
Data EngineeringML Engineering
About Autodesk
Autodesk builds design and engineering software used in architecture, construction, manufacturing, and media and entertainment. Its subscription-based portfolio includes AutoCAD, Revit, Fusion 360, Maya, and 3ds Max, along with cloud collaboration and simulation services. Founded in 1982 and headquartered in San Francisco, the public company trades on NASDAQ as ADSK and serves customers ranging from AEC firms to product designers and film and game studios.