Senior Machine Learning Engineer

WillHire

. As a Senior/Principal Machine Learning Engineer in Agent Factory, you’ll design and build the core ML systems behind Workday’s next generation of AI agents.

Posted 5/14/2026full-timeToronto • 🇨🇦 CanadaSenior💰 CA$188,000 - CA$282,000 per yearWebsite

Tech Stack

Tools & technologies

AWSCloudGoogle Cloud PlatformPyTorchTensorflow

About the role

Key responsibilities & impact

As a Senior/Principal Machine Learning Engineer in Agent Factory, you’ll design and build the core ML systems behind Workday’s next generation of AI agents.
Working within a small, senior, cross-functional pod, you’ll own how models, agent logic, and orchestration layers come together in production—across the full lifecycle from problem framing and data strategy to deployment, monitoring, and continuous improvement.
You’ll implement and evolve frameworks for LLM-powered agents, including RAG pipelines, workflow orchestration, evaluation, and feedback loops, ensuring solutions are scalable, observable, and enterprise-ready.
This role sits at the intersection of ML and platform engineering: partnering closely with software engineers, product managers, and data scientists to integrate agents deeply into the Workday stack.
You’ll stay hands-on with emerging techniques in agentic architectures while applying strong engineering judgment to turn them into systems that are reliable, explainable, and built to operate at global scale.

Requirements

What you’ll need

10+ years experience as a member of a data science, machine learning engineering, or other relevant software development team building applied machine learning products at scale
4+ years of professional experience in machine learning and deep learning frameworks & toolkits such as Pytorch, TensorFlow
6+ years of professional experience in building services to host machine learning models in production at scale
3+ years of demonstrated experience working with large language models (LLMs), text generation models, and/or graph neural network models for real-world use cases
6+ years of proven experience with cloud computing platforms (e.g. AWS, GCP, etc.)
Proven track record of successfully leading, mentoring, and/or managing ML Engineering teams, taking ownership of development lifecycle and sprint planning; fostering a culture of collaboration, transparency, innovation, and continuous improvement
Bachelor’s (Master’s or PhD preferred) degree in engineering, computer science, physics, math or equivalent

Benefits

Comp & perks

Health insurance
401(k) matching
Flexible work hours
Paid time off
Remote work options
Professional development opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdeep learninglarge language modelstext generation modelsgraph neural networksPytorchTensorFlowcloud computingmodel deploymentworkflow orchestration

Soft Skills

leadershipmentoringcollaborationtransparencyinnovationcontinuous improvementengineering judgmentproblem framingdata strategysprint planning

Certifications

Bachelor's degreeMaster's degreePhD