Senior Machine Learning Engineer, Fulfillment

Role overview

This senior machine learning engineering role develops and runs production models and services for high-impact business initiatives. You’ll own work across the ML lifecycle, from problem definition and data preparation to evaluation, launch, and monitoring, collaborating with product, data science, engineering, and business partners to deliver measurable results.

Responsibilities

- Formulate machine learning problems and build models and services for production use. - Manage the model lifecycle, including data preparation, feature development, training, offline evaluation, experimentation, rollout, and performance monitoring. - Build maintainable software, primarily in Python, using sound testing, profiling, debugging, documentation, and code review practices. - Work with stakeholders to define success measures, set priorities, and deliver solutions tied to customer or business outcomes. - Operate cloud-based ML services with suitable monitoring, alerting, observability, and reliability safeguards. - Apply statistical analysis and causal or counterfactual evaluation to assess model quality and inform launches. - Contribute to technical design, team practices, knowledge sharing, mentoring, and operational support.

Requirements

- Experience building machine learning systems and taking them into production. - Proficiency in Python and SQL, including production-quality coding and automated testing. - Experience across the ML lifecycle, from data and features through evaluation, deployment, and monitoring. - Experience operating machine learning services or data products in a cloud environment such as AWS or Google Cloud. - Familiarity with containerized production services, CI, monitoring, alerting, and incident response. - Ability to work with product, engineering, data science, and business partners to define requirements and achieve measurable outcomes.

Nice to have

A graduate degree in a quantitative or technical field is valued, as is experience with large-scale, high-throughput, or low-latency ML systems. Other useful experience includes distributed data processing, streaming, feature stores, model serving, orchestration, simulation or counterfactual evaluation, and a performance-oriented language such as C++, Java, or Go. Mentoring, technical leadership, and work in e-commerce, logistics, retail, marketplaces, advertising, or similar domains are also beneficial.

Benefits and work setup

The position is remote and currently open to candidates based in Ontario, Alberta, British Columbia, or Nova Scotia. Canadian base pay is listed at $180,000–$190,000 CAD, with eligibility for new-hire and annual equity grants. Compensation varies by work location and candidate experience.