Senior Machine Learning Engineer, Fulfillment

Description

The company is working to change how people buy groceries.

The Enterprise AI group builds intelligent products and platforms for customers, shoppers, retailers, brands, and internal partners, using machine learning, optimization, and software engineering.

The team is hiring a senior machine learning engineer to design, build, and ship production systems with measurable business effect, covering every stage from problem framing and data work to experiments, launch, and ongoing improvement.

The role suits an engineer who pairs solid machine learning skills with strong software engineering. You will work closely with product managers, data scientists, platform engineers, and business partners to turn open-ended opportunities into reliable, scalable solutions. The pace is quick and collaborative, and you can take on hard problems at large scale and see your systems used by real people.

Priorities, data, and business needs can change quickly, so you need to handle ambiguity, prioritize pragmatically, use sound technical judgment, and switch between strategy and hands-on implementation.

Responsibilities

• Design, build, and deploy machine learning models and services for Enterprise AI products and business initiatives.
• Own the full machine learning lifecycle: frame the problem, prepare data, develop features, train models, evaluate offline, run experiments, roll out to production, and monitor performance over time.
• Write maintainable, scalable Python and related code, with testing, profiling, code review, documentation, and operational readiness.
• Work with product, engineering, data science, and business teams to set success metrics, set priorities, and deliver outcomes that improve customer and business results.
• Build and run machine learning services on cloud infrastructure, with monitoring, alerting, observability, and safeguards for performance and reliability.
• Use experiments, statistical analysis, and causal or counterfactual methods to judge model quality and inform launch decisions.
• Shape technical direction through design reviews, architecture discussions, and careful weighing of modeling approaches, infrastructure choices, and tradeoffs.
• Strengthen the engineering culture by sharing knowledge, mentoring teammates, improving development practices, and supporting critical services in operations.

Requirements

• Strong foundation in machine learning and software engineering, with clear communication across disciplines.
• Ownership of work from idea through production, and motivation by measurable impact.
• Bachelor's degree in computer science, engineering, mathematics, statistics, or a related technical field, or equivalent practical experience.
• 5+ years of professional experience developing and deploying machine learning models or systems in production.
• Proficiency in Python and SQL, with production-quality code backed by automated testing, profiling, debugging, and code review.
• Hands-on experience with the full machine learning lifecycle, including data preparation, feature engineering, evaluation, experimentation, deployment, and production monitoring.
• Experience building and operating machine learning services or data products in a cloud environment such as Google Cloud Platform or Amazon Web Services.
• Experience with containers (Docker), continuous integration, monitoring, alerting, and incident response.
• Track record of working with product, engineering, data science, and business stakeholders to define requirements and deliver measurable results.
• Preferred: Master's or PhD in computer science, engineering, mathematics, statistics, or a related quantitative field.
• Preferred: experience designing machine learning systems for large-scale, high-throughput, or low-latency use.
• Preferred: familiarity with distributed data processing, streaming systems, feature stores, or real-time feature computation.
• Preferred: experience with model serving, orchestration, and ML platforms such as Kubernetes, Airflow, Spark, or similar tools.
• Preferred: experience designing simulation, experimentation, or counterfactual evaluation frameworks for machine learning and decision systems.
• Preferred: proficiency in a performance-oriented language such as C++, Java, or Go.
• Preferred: experience mentoring engineers, leading cross-functional projects, or setting technical direction for machine learning work.
• Preferred: experience in e-commerce, marketplaces, logistics, retail, advertising, or another domain with complex, changing business constraints.

Conditions

This is a remote role, and the base pay depends on your permanent work location.

Hiring is currently limited to Ontario, Alberta, British Columbia, and Nova Scotia in Canada.

For candidates based in Canada, the base pay range is $180,000 to $190,000 CAD.

Offers vary based on factors such as experience and the skills the role requires.

Employees choose where they work, whether from home, an office, or elsewhere, and stay connected through regular in-person events.

Benefits

• Eligible for a new hire equity grant and annual refresh equity grants.
• Compensation and benefits are competitive in each location where employees work.