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What type of employment is offered for this Senior Machine Learning Engineer, Search & Recommendations role?

Full-time or part-time position

What is the expected salary for this Senior Machine Learning Engineer, Search & Recommendations job?

Compensation will be discussed during the hiring process.

Senior Machine Learning Engineer, Search & Recommendations job near me in Canada Creek, Nova Scotia at Jobgether

Senior Machine Learning Engineer, Search & Recommendations

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Machine Learning Engineer, Search & Recommendations based in Canada.

This role sits at the core of a large-scale AI system powering search, discovery, and personalization across a high-traffic consumer platform. You will help design and evolve the ranking intelligence that determines how users find and engage with products, directly influencing key outcomes such as relevance, conversion, and long-term customer value. The environment is highly collaborative, bringing together machine learning engineers, data scientists, product managers, and infrastructure teams to build unified ranking systems at scale. You will work on multi-task and multi-objective models that balance user experience, business value, and marketplace health. The role also involves advancing value-aware and causal ML approaches that move beyond short-term optimization toward long-term impact. This is a highly technical, research-driven engineering role with strong production ownership and real-world business influence.

Accountabilities:

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.

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.

Benefits:

Competitive base salary ranging from $180,000 to $190,000 CAD (Canada-based compensation)
Annual equity refresh grants and new hire equity package eligibility
Fully remote-first flexibility within eligible Canadian provinces
Comprehensive health, dental, and vision insurance coverage
Flexible work environment with strong support for work-life balance
Paid time off, holidays, and parental leave benefits
Access to learning resources, research opportunities, and technical growth programs
Opportunity to work on large-scale ML systems impacting millions of users
Collaborative, research-driven engineering culture with strong ownership
Equity-aligned compensation structure tied to long-term company performance

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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Senior Machine Learning Engineer, Search & Recommendations in Canada Creek, Nova Scotia at Jobgether

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