Senior Machine Learning Engineer is a Machine Learning Engineer role (full-time). with Autodesk. in AMER - CANADA - ONTARIO - TORONTO - UNIVERSITY AVE, CA. Compensation shown: $123K–$180K. Imported listing (source: movojobs.ca). Apply on the employer's site (movojobs.ca).
Imported listing (source: movojobs.ca) · Apply on movojobs.ca
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Imported job description
Sourced from movojobs.ca
Autodesk is seeking a Senior Machine Learning Engineer to design, build, and scale production-grade AI/ML and Agentic AI systems for its Go-to-market intelligence function. The role focuses on end-to-end ML solutions, including feature engineering, model training, deployment, evaluation, and inference pipelines. You will collaborate with data engineers, scientists, and platform teams to deliver reusable intelligent data products at enterprise scale.
Responsibilities
Design, develop, test, deploy, and maintain production-grade ML pipelines for enterprise-scale use cases.
Develop reusable ML services and components.
Build robust model evaluation frameworks covering accuracy, relevance, groundedness, consistency, latency, throughput, robustness, and cost.
Design automated evaluation pipelines using deterministic metrics, model-based evaluation, curated datasets, regression testing, and human evaluation.
Build and maintain distributed processing pipelines for large volumes of structured and unstructured data.
Design and optimize distributed pipeline and cloud orchestration for large-scale AI workloads.
Implement resilient processing patterns including concurrency management, queue-based architectures, checkpointing, retries, failure recovery, rate limiting, and idempotent processing.
Optimize AI/ML systems for latency, throughput, scalability, infrastructure utilization, and model inference cost.
Autodesk is a global leader in design and make software, providing tools for architecture, engineering, construction, manufacturing, and media and entertainment industries. Its platform integrates technologies like AI, robotics, and generative design.
Partner with platform engineering teams to integrate AI applications with platforms, APIs, identity and access management, monitoring, and deployment infrastructure.
Implement MLOps and LLMOps practices, including model and prompt versioning, experiment tracking, evaluation, deployment automation, monitoring, rollback mechanisms, and lifecycle management.
Build comprehensive observability and monitoring mechanisms across ML pipelines.
Implement mechanisms to identify and manage model drift, data drift, quality degradation, and upstream data changes.
Build modular frameworks and reusable components for self-service patterns.
Work closely with data scientists, data engineers, analysts, product teams, and business stakeholders to translate business problems into ML architectures.
Translate complex ML system designs and trade-offs for technical and non-technical stakeholders.
Support experimentation and rapid prototyping while ensuring transition to maintainable production systems.
Contribute to engineering standards, reference architectures, design reviews, code reviews, technical documentation, and AI/ML engineering best practices.
Requirements
Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Information Systems, or a related technical discipline.
5+ years of machine learning engineering, data engineering, or related experience, including significant experience developing production systems.
Demonstrated experience designing and operating production ML systems rather than only experimentation or notebook-based model development.
Strong programming skills in Python, with the ability to develop modular, testable, maintainable, and production-quality software.
Working experience with Snowflake, including SnowSQL and Snowpipe, and Snowflake cost optimization.
Experience with workflow orchestration technologies such as Airflow or comparable frameworks.
Experience with data transformation tools like DBT.
Hands-on experience building and deploying machine learning inference pipelines and services.
Experience designing distributed data or ML processing pipelines for high-volume workloads.
Experience deploying workloads into a major cloud environment, preferably AWS, and working with cloud services for compute, storage, event processing, monitoring, and distributed execution.
Experience with Git-based software development workflows, code reviews, branching strategies, and collaborative engineering practices.
Familiarity with MLOps concepts, including experiment tracking, model lifecycle management, deployment, model monitoring, reproducibility, and versioning.
Experience working with structured and unstructured data and designing preprocessing, enrichment, and transformation pipelines.
Strong analytical, debugging, and problem-solving skills.
Strong written and verbal communication skills and ability to collaborate effectively with engineering, data science, product, and business stakeholders.
Ability to work effectively with geographically distributed teams across multiple time zones.
Familiarity with Agile/Scrum software development practices.
