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- Wayfair Canada ULCToronto, ON
- $330,408–$348,740 a year
- Ability to turn ambiguous catalog-quality opportunities into clear strategies and executable roadmaps.
- [3] How Wayfair used AI as a lab assistant to run 110…
- McKessonMississauga, ON L4V 1W2
- $122,100–$162,800 a year
- In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered.
- Our Base Pay Range for this position.
- View all McKesson jobs - Mississauga jobs - Forward Deployed Engineer jobs in Mississauga, ON
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- McKessonMississauga, ON L4V 1W2
- $122,100–$162,800 a year
- In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered.
- Our Base Pay Range for this position.
- View all McKesson jobs - Mississauga jobs - Forward Deployed Engineer jobs in Mississauga, ON
- Salary Search: Forward Deployed Engineering, US Oncology salaries
- See popular questions & answers about McKesson
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Machine Learning Manager - Catalog Science
Toronto, ON
•
Hybrid work
$330,408–$348,740 a year
Machine Learning Manager - Catalog Science
Job details
$330,408–$348,740 a year
Tuition reimbursement, Paid time off, Vision care, Dental care, Life insurance, Disability insurance, Designated paid holidays
Hybrid work in Toronto, ON
Full job description
Salary Range: $330,408 - $348,740 CAD per year. Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the annual base salary only and does not include equity
Candidates for this position are preferred to be based in Toronto and will be expected to comply with their team's hybrid work schedule requirements. Our team's are in office Tuesday, Wednesday, Thursday and remote on Monday and Fridays.
Who We Are
Wayfair is an online retail platform with the mission to enable everyone to live in a home they love. Delivering on that mission at global scale requires a high-quality, trustworthy product catalog that customers and internal systems can rely on.
The Catalog Health Science organization builds the machine learning systems that power catalog quality end-to-end: how products enter the catalog, how they are structured, and how they are presented to customers.
What You’ll Do
Set Strategy and Direction for AI-Powered Catalog Intelligence
*
Define and own the ML and AI strategy for catalog intelligence, aligned with Catalog, Product, Engineering, Operations, and broader AI priorities.
*
Lead a portfolio of capabilities that improve product content quality, including:
*
AI-Powered Catalog Enrichment: extracting, validating, enriching, and reconciling structured product information at scale.
*
Media Content Intelligence: understanding product images, documents, and other media to improve content quality and presentation.
*
Customer Voice and UGC: curating, summarizing, moderating, and ranking reviews, customer images, and related feedback.
*
Identify opportunities to apply machine learning, generative AI, multimodal models, and agentic systems to improve product-data accuracy, customer trust, supplier experience, and operational efficiency.
*
Partner with Product, Engineering, Analytics, Merchandising, and Catalog Operations to prioritize work, define success metrics, and align roadmaps.
*
Balance near-term business delivery with reusable technical capabilities, evaluation systems, and platform investments.
Build, Lead, and Develop a High-Performing Science Team
*
Manage and develop a team of Machine Learning Scientists working across experimentation, model development, evaluation, productionization, and iteration.
*
Set clear priorities and ownership across multiple workstreams, enabling the team to deliver measurable business impact reliably.
*
Provide hands-on technical leadership through reviews of project proposals, model and system designs, experiment plans, evaluation frameworks, and code.
*
Dive into complex, ambiguous, or high-risk problems when needed.
*
Coach scientists to own work end to end: problem definition, stakeholder alignment, experimentation, launch, monitoring, and iteration.
*
Raise the bar for scientific rigor, technical quality, clear communication, and business impact.
*
Partner with recruiting and other Science leaders to hire, onboard, and grow talent across levels.
*
Foster an inclusive, collaborative environment with high standards, thoughtful debate, and continuous learning.
Drive Cross-Functional Execution and Change
*
Serve as a primary Science leader across Catalog, Product, Engineering, Merchandising, Operations, and partner teams.
*
Translate broad catalog-quality opportunities into clear programs with defined milestones, dependencies, ownership, and outcomes.
*
Partner with Engineering to ensure that models, AI systems, data pipelines, and serving systems are scalable, observable, reliable, and cost-efficient.
*
Guide investments in shared AI capabilities, evaluation tooling, experimentation, monitoring, and feedback systems.
*
Integrate ML and AI decisions into production workflows, including human-review and exception-handling processes.
*
Build feedback loops using human review, supplier input, customer signals, and production outcomes to improve systems over time.
*
Clearly communicate progress, risks, and technical tradeoffs to stakeholders and senior leaders.
We Are a Match Because You Have
Core Experience
*
PhD with 5+ years, MS with 9+ years, or BS with 11+ years of experience in computer science, machine learning, statistics, or another quantitative STEM field.
*
Experience leading applied ML or data science teams and delivering production systems with meaningful business impact.
*
Strong foundations in machine learning, statistics, experimentation, and evaluation.
*
Experience managing and developing ML Scientists, Data Scientists, or comparable technical roles, ideally on a team of 5-10 people.
*
A track record of leading cross-functional programs spanning Science, Engineering, Product, Operations, and business stakeholders.
Technical Depth
*
Technical depth to coach and review scientists’ work, while contributing directly when necessary.
*
Experience building and evaluating production ML systems using large, complex datasets.
*
Proficiency in Python and the modern ML ecosystem, such as PyTorch, TensorFlow, XGBoost, or similar tools.
*
Experience with structured product data, text, images, documents, customer feedback, or other multimodal data.
*
Hands-on experience with generative AI systems, including commercial APIs or open-source models, prompt design, retrieval, fine-tuning, and evaluation.
*
Experience developing or evaluating multi-step AI and agentic workflows, including systems with non-deterministic or human-in-the-loop behavior.
*
Experience using agentic coding harnesses such as Cursor, Codex, Devin, or similar tools to accelerate research, experimentation, prototyping, and production delivery. This role is expected to use these tools extensively and help establish effective team practices for doing so.
*
Familiarity with cloud platforms, MLOps, experiment tracking, model monitoring, and production observability.
Leadership and Communication
*
Ability to turn ambiguous catalog-quality opportunities into clear strategies and executable roadmaps.
*
Strong written and verbal communication skills, including the ability to explain technical tradeoffs to non-technical audiences.
*
Sound judgment in balancing immediate delivery with longer-term platform, architecture, and talent investments.
*
A growth mindset, bias toward action, and commitment to using evidence to adjust course.
Why You’ll Love Working With Us
*
Lead a high-impact ML and GenAI team improving product content across one of the world’s largest retail catalogs.
*
Shape AI-powered systems for catalog enrichment, validation, media understanding, and customer-feedback intelligence.
*
Work closely with talented Science, Engineering, Product, Merchandising, and Operations partners.
*
Build the technical and organizational foundations for responsible, production-scale AI at Wayfair.
Learn More About the Role
*
[1] How Wayfair uses OpenAI
*
[2] How Wayfair uses Cursor
*
[3] How Wayfair used AI as a lab assistant to run 110 experiments in three days
Benefits & Perks
Wayfair offers a comprehensive benefits package that includes:
Visible links
1. https://openai.com/index/wayfair/
2. https://cursor.com/blog/wayfair
3. https://www.aboutwayfair.com/careers/tech-blog/how-we-used-ai-as-a-lab-assistant-to-run-110-experiments-in-three-days-and-cut-projected-tag-validation-cost-by-94
Candidates for this position are preferred to be based in Toronto and will be expected to comply with their team's hybrid work schedule requirements. Our team's are in office Tuesday, Wednesday, Thursday and remote on Monday and Fridays.
Who We Are
Wayfair is an online retail platform with the mission to enable everyone to live in a home they love. Delivering on that mission at global scale requires a high-quality, trustworthy product catalog that customers and internal systems can rely on.
The Catalog Health Science organization builds the machine learning systems that power catalog quality end-to-end: how products enter the catalog, how they are structured, and how they are presented to customers.
What You’ll Do
Set Strategy and Direction for AI-Powered Catalog Intelligence
*
Define and own the ML and AI strategy for catalog intelligence, aligned with Catalog, Product, Engineering, Operations, and broader AI priorities.
*
Lead a portfolio of capabilities that improve product content quality, including:
*
AI-Powered Catalog Enrichment: extracting, validating, enriching, and reconciling structured product information at scale.
*
Media Content Intelligence: understanding product images, documents, and other media to improve content quality and presentation.
*
Customer Voice and UGC: curating, summarizing, moderating, and ranking reviews, customer images, and related feedback.
*
Identify opportunities to apply machine learning, generative AI, multimodal models, and agentic systems to improve product-data accuracy, customer trust, supplier experience, and operational efficiency.
*
Partner with Product, Engineering, Analytics, Merchandising, and Catalog Operations to prioritize work, define success metrics, and align roadmaps.
*
Balance near-term business delivery with reusable technical capabilities, evaluation systems, and platform investments.
Build, Lead, and Develop a High-Performing Science Team
*
Manage and develop a team of Machine Learning Scientists working across experimentation, model development, evaluation, productionization, and iteration.
*
Set clear priorities and ownership across multiple workstreams, enabling the team to deliver measurable business impact reliably.
*
Provide hands-on technical leadership through reviews of project proposals, model and system designs, experiment plans, evaluation frameworks, and code.
*
Dive into complex, ambiguous, or high-risk problems when needed.
*
Coach scientists to own work end to end: problem definition, stakeholder alignment, experimentation, launch, monitoring, and iteration.
*
Raise the bar for scientific rigor, technical quality, clear communication, and business impact.
*
Partner with recruiting and other Science leaders to hire, onboard, and grow talent across levels.
*
Foster an inclusive, collaborative environment with high standards, thoughtful debate, and continuous learning.
Drive Cross-Functional Execution and Change
*
Serve as a primary Science leader across Catalog, Product, Engineering, Merchandising, Operations, and partner teams.
*
Translate broad catalog-quality opportunities into clear programs with defined milestones, dependencies, ownership, and outcomes.
*
Partner with Engineering to ensure that models, AI systems, data pipelines, and serving systems are scalable, observable, reliable, and cost-efficient.
*
Guide investments in shared AI capabilities, evaluation tooling, experimentation, monitoring, and feedback systems.
*
Integrate ML and AI decisions into production workflows, including human-review and exception-handling processes.
*
Build feedback loops using human review, supplier input, customer signals, and production outcomes to improve systems over time.
*
Clearly communicate progress, risks, and technical tradeoffs to stakeholders and senior leaders.
We Are a Match Because You Have
Core Experience
*
PhD with 5+ years, MS with 9+ years, or BS with 11+ years of experience in computer science, machine learning, statistics, or another quantitative STEM field.
*
Experience leading applied ML or data science teams and delivering production systems with meaningful business impact.
*
Strong foundations in machine learning, statistics, experimentation, and evaluation.
*
Experience managing and developing ML Scientists, Data Scientists, or comparable technical roles, ideally on a team of 5-10 people.
*
A track record of leading cross-functional programs spanning Science, Engineering, Product, Operations, and business stakeholders.
Technical Depth
*
Technical depth to coach and review scientists’ work, while contributing directly when necessary.
*
Experience building and evaluating production ML systems using large, complex datasets.
*
Proficiency in Python and the modern ML ecosystem, such as PyTorch, TensorFlow, XGBoost, or similar tools.
*
Experience with structured product data, text, images, documents, customer feedback, or other multimodal data.
*
Hands-on experience with generative AI systems, including commercial APIs or open-source models, prompt design, retrieval, fine-tuning, and evaluation.
*
Experience developing or evaluating multi-step AI and agentic workflows, including systems with non-deterministic or human-in-the-loop behavior.
*
Experience using agentic coding harnesses such as Cursor, Codex, Devin, or similar tools to accelerate research, experimentation, prototyping, and production delivery. This role is expected to use these tools extensively and help establish effective team practices for doing so.
*
Familiarity with cloud platforms, MLOps, experiment tracking, model monitoring, and production observability.
Leadership and Communication
*
Ability to turn ambiguous catalog-quality opportunities into clear strategies and executable roadmaps.
*
Strong written and verbal communication skills, including the ability to explain technical tradeoffs to non-technical audiences.
*
Sound judgment in balancing immediate delivery with longer-term platform, architecture, and talent investments.
*
A growth mindset, bias toward action, and commitment to using evidence to adjust course.
Why You’ll Love Working With Us
*
Lead a high-impact ML and GenAI team improving product content across one of the world’s largest retail catalogs.
*
Shape AI-powered systems for catalog enrichment, validation, media understanding, and customer-feedback intelligence.
*
Work closely with talented Science, Engineering, Product, Merchandising, and Operations partners.
*
Build the technical and organizational foundations for responsible, production-scale AI at Wayfair.
Learn More About the Role
*
[1] How Wayfair uses OpenAI
*
[2] How Wayfair uses Cursor
*
[3] How Wayfair used AI as a lab assistant to run 110 experiments in three days
Benefits & Perks
Wayfair offers a comprehensive benefits package that includes:
- Time Off
- Paid Holidays
- Unlimited Paid Time Off (PTO)
- Health & Wellness
- Full Health Benefits (Medical, Dental, Vision, HSA/FSA)
- Life Insurance
- Short Term & Long Term Disability
- Global wellbeing offerings such as gym/fitness discounts and mental health support
- Financial Growth & Security
- 401(k) matching (Employee Matching Program)
- Tuition reimbursement
- Financial health education resources and tax-advantaged accounts
- Family Support
- Family planning support
- Parental leave
- Global surrogacy & adoption policy
- Professional Development & Recognition
- Rewards & recognition programs
- Global employee anniversary awards
- Paid volunteer opportunities
- Unique Perks
- Employee discount
- Local perks in select office locations
- Team and pod outings
Visible links
1. https://openai.com/index/wayfair/
2. https://cursor.com/blog/wayfair
3. https://www.aboutwayfair.com/careers/tech-blog/how-we-used-ai-as-a-lab-assistant-to-run-110-experiments-in-three-days-and-cut-projected-tag-validation-cost-by-94
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