Senior Machine Learning Engineer, Search & Recommendations
New
Canada-based compensation; Fully remote-first flexibility within eligible Canadian provincesFull-TimeSenior
Salary180,000 - 190,000 CAD per year
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Job Details
- Experience
- 4+ years of industry experience applying machine learning at scale (or 2+ years with a PhD)
- Required Skills
- PythonSQLMachine LearningPyTorchPandasTensorflowA/B testing
Requirements
- 4+ years of industry experience applying machine learning at scale (or 2+ years with a PhD), with proven impact on ranking or recommendation systems.
- Strong experience with multi-objective optimization in production environments, balancing relevance, revenue, and user experience.
- Proficiency in Python and strong data skills using SQL, Pandas, and related tools.
- Hands-on experience with ML frameworks such as TensorFlow or PyTorch and classical ML methods like gradient boosting (e.g., XGBoost).
- Solid understanding of ranking systems, personalization, and recommendation architectures.
- Experience with online experimentation, A/B testing, and advanced evaluation methods beyond CTR-based metrics.
- Familiarity with multi-task learning architectures (MMOE, PLE, shared encoders) and/or causal inference, uplift modeling, and contextual bandits.
- Experience building or optimizing low-latency ML systems, including feature pipelines, caching, retrieval systems, and inference optimization.
- Exposure to LLMs for feature enrichment, embeddings, or retrieval augmentation is a strong plus.
- Strong communication skills with the ability to collaborate across technical and non-technical teams.
Responsibilities
- Architect and develop scalable ranking systems that unify search, recommendations, ads, and merchandising into a single multi-objective framework.
- Design and implement multi-task learning models (e.g., shared encoders, MMOE/PLE architectures) to jointly optimize relevance, conversion, margin, churn risk, and other business signals.
- Build and improve value-aware and long-horizon optimization models, including uplift and causal inference approaches to maximize incremental impact and LTV.
- Develop and maintain production-grade ranking pipelines, including inference systems, re-ranking layers, and constraint-aware decisioning.
- Enhance search and discovery experiences, including personalized autosuggest and retrieval systems powered by ML and LLM-enhanced features.
- Design and execute large-scale online experiments, A/B testing frameworks, and counterfactual evaluation methods to measure impact beyond short-term metrics.
- Collaborate cross-functionally with Ads, Product, Infrastructure, and Design teams to translate business objectives into ranking strategies and measurable outcomes.
- Mentor and guide other ML engineers on ranking systems, causal modeling, and scalable ML infrastructure.
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