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ZI
electrical engineer
Ziyutec Inc.
Vancouver, BC
- $80,000 to $135,000 CAD / year
- Full-time
- On-site
…interested in strong intermediate engineers as well as experienced senior engineers. Responsibilities and compensation will reflect the successful candidate's experience and capabilities.…
Added 4 mins ago
SD
Senior Specialist, Beauty Operations - Contract
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Toronto, ON
- Full-time
Added 4 mins ago
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Senior Manager, Learning, Development and Engagement
Shoppers Drug Mart
Toronto, ON
- Full-time
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Added 4 mins ago
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Senior Director, Pharmacy Experience and Transformation
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Senior Infrastructure Engineer (Linux & Cloud Automation)
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Job Details: Senior Infrastructure Engineer (Linux & Cloud Automation)
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Sr. Director, Pharmacy Services and Virtual Care
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Chief Technology Officer
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RQ10989 – Business Architect – Senior
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RQ11619 – Business Analyst – Senior
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Associate Director, Market Access & Reimbursement Strategy Speciality Portfolio
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RQ11539 – UX Designer – Senior
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RQ11518 – Business Analyst – Senior
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Added 20 mins ago
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RQ11541 – UX Designer – Senior
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Added 22 mins ago
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Autodesk

Senior Machine Learning Engineer

Toronto, ON, Canada
AMER - Canada - Ontario - Toronto - University Ave, Canada

For Canada based roles, we expect a starting base salary between $123,000 and $180,400. · Full-time

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Job details

Pay: For Canada based roles, we expect a starting base salary between $123,000 and $180,400.
Job type: Full-time
Experience: 5+ years of machine learning engineering, or data engineering, or related experience, including significant experience developing production systems
Posting language: English
Posted: Oct 7, 2026

Original job description

Job Requisition ID #

26WD101236

Position Overview

We are looking for an exceptional Machine Learning Engineer to design, build, operationalize, and scale production-grade AI/ML and Agentic AI systems at Autodesk For Go-to-market intelligence function.

The mission of the team is to empower decision makers and the broader data communities through trusted data assets and scalable self-serve intelligence. The focus of this role will be engineering end-to-end AI/ML solutions - including feature engineering, data cleansing, contributing in model training process, model deployment, model validations, model evaluation, inference pipelines, and production orchestration.

You will work at the intersection of machine learning, data & analytics engineering, You will collaborate closely with data engineers, data scientists, analysts, platform teams, and business stakeholders to deliver reusable intelligent data products at enterprise scale.

The role requires a strong engineering mindset, hands-on experience building production ML systems, and the ability to evaluate and integrate rapidly evolving AI technologies while maintaining high standards for quality, observability, security, governance, cost efficiency, and operational reliability.

Responsibilities

Design, develop, test, deploy, and maintain production-grade ML pipelines supporting enterprise-scale use cases
Develop reusable ML services and components
Develop robust model evaluation frameworks covering dimensions such as 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 where appropriate
Build and maintain distributed processing pipelines capable of handling large volumes of documents, web content, structured data, and unstructured data efficiently
Design and optimize distributed pipeline and cloud orchestration for large-scale AI workloads using appropriate workflow orchestration and cloud-native technologies
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
Partner with platform engineering teams to integrate AI applications with the relevant platforms, APIs, identity and access management, monitoring, and deployment infrastructure
Implement appropriate 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, covering pipeline health, model performance, data quality, failures, and cost
Implement mechanisms to identify and manage model drift, data drift, quality degradation, and upstream data changes
Build modular frameworks and reusable components that enable teams to develop new capabilities through self-service patterns rather than one-off implementations
Work closely with data scientists, data engineers, analysts, product teams, and business stakeholders to translate business problems into appropriate ML architectures and implementation strategies
Translate complex ML system designs, model behavior, limitations, and trade-offs into business-appropriate representations for technical and non-technical stakeholders
Support experimentation and rapid prototyping while ensuring successful solutions can transition into maintainable, production-grade systems
Contribute to engineering standards, reference architectures, design reviews, code reviews, technical documentation, and AI/ML engineering best practices

Minimum Qualifications

Bachelor's degree in Computer Science, Engineering, Machine Learning, Data Science, Information Systems, or a related technical discipline
5+ years of machine learning engineering, or 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, Hands-on experience with Snowflake utilities, Snow SQL, Snow Pipe. Must have worked on Snowflake Cost optimization scenarios
Experience with workflow orchestration technologies such as Airflow or comparable orchestration frameworks
Have experience on 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 with the ability to diagnose issues across application, model, pipeline, and infrastructure layers
Strong written and verbal communication skills and the 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.
Experience working with remote teams spread across multiple time-zones

Learn More

About Autodesk

Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.

We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.

When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!

Salary transparency

Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $123,000 and $180,400. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.

Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging

In-Person Onboarding and Identity Verification

This role may require in-person onboarding and/or in-person ID verification.

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More about this job

Responsibilities

Design, build, and scale production-grade AI/ML and Agentic AI systems to support go-to-market intelligence. Collaborate with cross-functional teams to develop reusable ML services, evaluation frameworks, and robust data pipelines at enterprise scale.

Requirements

Requires a bachelor's degree in a technical field and at least 5 years of experience in machine learning or data engineering. Candidates must have strong Python programming skills and hands-on experience with Snowflake, cloud environments, and MLOps practices.

Skills

Python
Snowflake
Snow SQL
Snow Pipe
Airflow
DBT
Machine Learning
MLOps
LLMOps
Cloud orchestration
AWS
Data engineering
Distributed systems
Model evaluation
Feature engineering
Data pipelines

Benefits

Annual cash bonuses
Stock grants

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
AI
Agentic AI
Python
Snowflake
Snow SQL
Snow Pipe
Airflow
DBT
MLOps
LLMOps
AWS
Cloud Computing
Data Engineering
Feature Engineering
Model Deployment
Inference Pipelines
Distributed Systems
Data Governance
Observability
Agile
Scrum
Data Science
Automation
Scalability
Model Drift
Prompt Versioning
Infrastructure
API
Identity and Access Management

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