Job Code: R2000564340


Job Description: 

At Loblaw Digital, we know that our customers expect the best from us. Whether that means building the best, most innovative online shopping experience, or designing an app that will impact the lives of people across the country, we’re up for the challenge. From our office in Downtown Toronto, we’ve created leading eCommerce experiences in the online grocery shopping, beauty, pharmacy, and apparel spaces, and we’re only just getting started.

Why is this role important?

We are looking for a Staff Machine Learning Engineer to help shape the future of personalization at Loblaw Digital. This is a high-impact, hands-on technical leadership role that goes beyond individual contribution—your work will guide the architecture, scalability, and innovation of ML systems that power personalized experiences for millions of Canadians.

You will collaborate with engineering leaders, product managers and ML practitioners to develop cutting-edge systems—including multimodal recommendation engines, generative AI applications, and real-time inference platforms. As a Staff Engineer, you will act as a force multiplier: solving technically complex problems, mentoring senior engineers, and driving long-term strategy for our ML platforms.

As a Staff Machine Learning Engineer, you’ll be empowered to shape the future of personalized commerce through your technical leadership, curiosity, and impact. You'll have the opportunity to:

• Work with world-class engineers, data scientists, and product teams
• Lead development on cutting-edge ML projects that directly affect millions of customers.
• Make strategic decisions that shape our personalization roadmap and ML stack.
• Elevate the technical capabilities of the organization through mentorship and innovation.

What You'll Do:

• Define and drive the technical vision for personalization and recommendation systems, with a focus on scalability, performance, and innovation.
• Architect and lead development of large-scale, production-grade ML systems, including deep learning models, real-time inference services, and end-to-end ML pipelines.
• Champion the use of multimodal data (text, image, behavioral, contextual) to power highly personalized recommendations.
• Lead initiatives in generative AI, driving product discovery experiences through LLMs and related models.
• Collaborate closely with Engineering Managers, ML Engineers, and Product to translate complex business goals into robust, scalable systems.
• Set standards for technical excellence, contributing to architecture reviews, code quality, observability, and performance.
• Mentor senior engineers, conduct design and code reviews, and help teams navigate architectural trade-offs.
• Partner with infrastructure teams to evolve our ML platform and tooling using GCP, Vertex AI, BigQuery, Airflow (Cloud Composer), Docker, and Kubernetes.
• Rapidly prototype and develop proof-of-concept projects to explore and validate new ideas
• Stay current on advancements in AI/ML and continuously evaluate new technologies for potential adoption.

Does This Sound Like You?

• 10+ years of experience in ML engineering, with a track record of technical leadership on high-impact ML systems.
• Proven expertise in recommendation systems, personalization, or search ranking.
• Deep understanding of deep learning (especially with PyTorch or TensorFlow) and ML infrastructure at scale.
• Experience building real-time inference systems and working with multimodal architectures.
• Proficient in Python, SQL, and cloud-native development (preferably GCP).
• Experience designing and deploying robust, observable, and scalable systems.
• Familiar with tools like Docker, Kubernetes, Airflow (Cloud Composer), and distributed compute frameworks (e.g., Spark).
• Able to think strategically and influence decisions beyond your immediate team.
• Strong communication skills—you can explain complex technical concepts to non-technical stakeholders and collaborate across disciplines.
• Passionate about mentorship, knowledge sharing, and fostering an inclusive, high-performing technical culture.