Senior Machine Learning Engineer, Search & Recommendations
TLDR
Develop and optimize scalable ranking systems powering search, recommendations, and personalization for a high-traffic platform, balancing relevance, business value, and long-term impact.
- 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.
- 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.
- 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
Requirements:
Benefits:
Benefits
Equity Compensation
Equity-aligned compensation structure tied to long-term company performance
Flexible Work Hours
Flexible work environment with strong support for work-life balance
Health Insurance
Comprehensive health, dental, and vision insurance coverage
Learning Budget
Access to learning resources, research opportunities, and technical growth programs
Research-driven culture
Collaborative, research-driven engineering culture with strong ownership
Paid Time Off
Paid time off, holidays, and parental leave benefits
Remote-Friendly
Fully remote-first flexibility within eligible Canadian provinces
Jobgether runs the largest remote job platform, effectively linking job seekers with over 200,000 flexible and remote opportunities that match their unique skills and preferences. Our focus is on enhancing the hiring process, ensuring efficiency while prioritizing the candidate experience, particularly in the growing health and wellness sector.
- Founded
- Founded 2020
- Employees
- 11-50 employees
- Industry
- Professional Services