Senior Production ML Engineer

Autodesk, Inc.; Toronto; Hybrid; CAD 123,000 - 180,000; Full time

Job summary

Autodesk, Inc. is seeking a senior Machine Learning Engineer to design, build, and productionize AI/ML systems at enterprise scale. You will own end-to-end pipelines, feature engineering, data cleansing, model deployment, and robust evaluation across accuracy, latency, and cost.

Collaboration spans data engineers, scientists, analysts, and platform teams to deliver reusable intelligent data products. The role demands strong software engineering practices, experience operating production ML

Qualifications

• Bachelor's degree in Computer Science, Engineering, ML, Data Science, Information Systems, or related technical discipline.
• 5+ years of machine learning engineering, or data engineering, or related experience, including production systems.
• Strong programming skills in Python, with modular, testable, production-quality software.
• Hands-on experience with Snowflake utilities, Snow SQL, Snow Pipe and Snowflake cost optimization.

Responsibilities

• Design, develop, test, deploy, and maintain production-grade ML pipelines for enterprise-scale use cases.
• Develop reusable ML services and components and evaluation frameworks with metrics for accuracy, relevance, groundedness, and latency.
• Build distributed processing pipelines and cloud orchestration for large-scale AI workloads.
• Implement MLOps and LLMOps practices: versioning, experiment tracking, deployment automation, monitoring, and lifecycle management.
• Establish observability for pipelines, data quality, failures, and cost; manage model drift and data drift.

Skills

Python

ML Engineering

Data Engineering

Airflow

DBT

MLOps

Experiment tracking

Model lifecycle management

Git workflows

Communication skills

Education

Bachelor's degree in CS/Engineering/Data Science

Tools

Snowflake

Snow SQL

Snow Pipe

AWS

Airflow

Job description

Autodesk, Inc. is seeking a senior Machine Learning Engineer to design, build, and productionize AI/ML systems at enterprise scale. You will own end-to-end pipelines, feature engineering, data cleansing, model deployment, and robust evaluation across accuracy, latency, and cost.

Collaboration spans data engineers, scientists, analysts, and platform teams to deliver reusable intelligent data products. The role demands strong software engineering practices, experience operating production ML