Senior Machine Learning Engineer
@ Kraft Heinz

Toronto, ON
$112,850
On Site
Full Time
Posted 23 hours ago

Your Application Journey

Personalized Resume
Apply
Email Hiring Manager
Interview

Email Hiring Manager

XXXXXXXXXX XXXXXXXXXXX XXXXXXXXXX******* @kraftheinz.com
Recommended after applying

Job Details

Overview

Kraft Heinz is seeking a Senior Machine Learning Engineer. As part of the DIPP – Decision Intelligence Products & Platforms ML Engineering team, you will drive value through scalable ML systems and robust MLOps practices across Supply Chain, Manufacturing, Commercial, R&D, HR, and Marketing.

MLOps & Model Lifecycle Automation

Lead end-to-end model management including versioning, testing, deployment, monitoring, and CI/CD pipeline establishment.

  • Implement model monitoring using Aporia for performance, bias, and drift.
  • Create governance frameworks ensuring secure and scalable deployments.

ML Platform Engineering & Architecture

Contribute to designing the internal ML platform with reusable components and documentation to ensure robust experimentation and model auditability.

Applied Machine Learning

Develop, deploy, and operationalize predictive models improving efficiency, forecasting demand, and managing risks, while collaborating closely with data scientists and SMEs.

Benefits & Rewards

Enjoy a holistic wellness package, performance-based bonus, immediate benefits upon hire, and a variety of support programs tailored for employees and their families.

Location

This position is located at Toronto - Queen's Quay on the Headquarters campus.

Key skills/competency

  • MLOps
  • Machine Learning
  • CI/CD
  • Model Governance
  • Aporia
  • TensorFlow
  • PyTorch
  • Cloud (AWS/Azure/GCP)
  • Snowflake
  • Responsible AI

How to Get Hired at Kraft Heinz

🎯 Tips for Getting Hired

  • Customize your resume: Tailor skills to ML engineering and MLOps practices.
  • Highlight relevant experience: Emphasize production-scale ML projects.
  • Research Kraft Heinz: Investigate company values and innovations.
  • Prepare for technical interviews: Review CI/CD, tensor frameworks, and cloud platforms.

📝 Interview Preparation Advice

Technical Preparation

Review ML frameworks, especially TensorFlow and PyTorch.
Practice building CI/CD pipelines for ML projects.
Study cloud integration with AWS, Azure, and GCP.
Learn configuration of monitoring tools like Aporia.

Behavioral Questions

Explain a challenging project in ML deployments.
Describe a time you managed cross-functional collaboration.
Discuss handling feedback on production issues.
Share an experience enhancing model governance.

Frequently Asked Questions