Sr Engineering Manager Data and ML

EBay

Job Location:

Toronto - Canada

Monthly Salary: Not Disclosed

Posted on: 15 hours ago

Vacancies: 1 Vacancy

Job Summary

At eBay were more than a global ecommerce leader were changing the way the world shops and sells. Our platform empowers millions of buyers and sellers in more than 190 markets around the world. Were committed to pushing boundaries and leaving our mark as we reinvent the future of ecommerce for enthusiasts.

Our customers are our compass authenticity thrives bold ideas are welcome and everyone can bring their unique selves to work every day. Were in this together sustaining the future of our customers our company and our planet.

Join a team of passionate thinkers innovators and dreamers and help us connect people and build communities to create economic opportunity for all.

About the team and the role

We are looking for a Senior Engineering Manager to lead a multidisciplinary team within eBays Selling Engineering organization that powers the data and intelligence behind our seller experiences. The team is composed of ML engineers data and storage engineers backend engineers and applied researchers who together build the systems and models that help millions of sellers price smarter source better understand their customers and grow their business on eBay.

Your team owns end-to-end product capabilities from large-scale data pipelines and serving systems to the ML and LLM-driven models that turn raw seller listing and transaction data into actionable insights. Typical problem spaces include pricing intelligence sell-through and demand recommendations seller transaction analytics product research and seller business performance signals.

You will play a critical role in shaping the data architecture ML/LLM strategy and product execution of the team. This is a hands-on leadership role that requires active participation in design technical reviews and applied AI direction alongside team leadership responsibilities.

What you will accomplish

**AIfirst.** You treat agentic AI and modern ML/LLM systems as a core design and operating lever using them as a force multiplier for engineering productivity model quality and customer impact while setting clear guardrails for responsible AI safety and ethics.
**Generative leadership.** You lead with outcomes over outputs optimize flow (shorter lead times limited WIP fast feedback) create psychological safety for intelligent failure invite rather than inflict change and practice servant leadership grounded in continuous experimentation and improvement.
Lead and grow a high-performing team of 812 building seller-facing data and intelligence products.
Stay actively involved in system design model architecture reviews and hands-on technical contribution across data pipelines serving systems and ML/LLM workflows (Java/Kotlin Python Spark/Scala or similar).
Own the ML and applied AI roadmap for the team including classical ML models ranking and recommendation systems embeddings and LLM-based understanding for seller insights pricing sell-through and product research.
Drive best practices in data engineering and ML systems including data quality lineage reproducibility model lifecycle (training evaluation deployment monitoring) and responsible AI.
Provide technical leadership through mentorship design and code reviews model reviews and architectural guidance across backend data ML and research workstreams.
Own and drive architectural decisions for distributed data systems and ML serving with strong reasoning about consistency freshness latency cost and scale trade-offs.
Partner closely with Product Design Data Science and Engineering and partner engineering teams to align on goals prioritization and execution.
Foster a strong engineering and applied research culture centered on ownership continuous improvement scientific rigor and technical excellence.

What you will bring

BS in Computer Science Engineering or equivalent technical degree.
12 years of software engineering experience with deep expertise across data and/or ML systems including:
Object-Oriented Programming (OOP) and SOLID principles.
Designing and operating large-scale data pipelines (batch and streaming ETL) on platforms such as Spark Hadoop Airflow or equivalents.
Hands-on experience with multiple storage paradigms search engines (OpenSearch / Elasticsearch) document databases key-value stores and relational/analytical stores and the trade-offs between them.
Designing and building distributed systems with strong understanding of consistency durability freshness and availability trade-offs.
Scalable API and service design (GQL REST and service-oriented architectures) for data- and ML-backed products.
4 years of experience managing engineering teams that include service ML engineers data engineers and/or applied researchers building strong teams and handling performance and growth with empathy and clarity.
Solid working knowledge of the modern ML lifecycle feature engineering training evaluation deployment monitoring and retraining and of how ML and LLM-driven capabilities are productized into customer-facing experiences.
Strong hands-on technical foundation this role requires active technical contribution in design reviews and prototyping.
Excellent knowledge of software design principles data architecture and ML system design.
Experience working in Agile environments (Scrum Kanban) including sprint planning roadmap delivery and iterative experimentation.
Proficiency with tools like Jira (or equivalent) for backlog management and delivery tracking.
Strong planning and prioritization skills including breaking down complex data and ML initiatives into manageable measurable milestones.
Ability to provide accurate estimations and balance delivery speed model quality and operational cost.
Experience translating product and business requirements into technical and modeling designs and execution plans.
Excellent communication skills with the ability to work effectively across engineering research product and business stakeholders.
Strong problem-solving skills with the ability to proactively identify and mitigate risks dependencies and data/model regressions.

## Bonus Skills

Experience building seller- merchant- or marketplace-facing data and intelligence products (pricing demand recommendations business analytics).
Hands-on experience with LLMs and applied GenAI RAG architectures embeddings fine-tuning evaluation and agentic workflows in production.
Experience with vector databases and hybrid (lexical semantic) search at scale.
Familiarity with feature stores ML platforms (e.g. MLflow Kubeflow SageMaker or equivalents) and model serving frameworks.
Experience with cloud-native architectures Kubernetes Docker and containerized deployments.
Experience with CI/CD pipelines data observability and ML monitoring (data drift model performance cost).
Understanding of distributed system observability (logging tracing metrics) for both services and ML pipelines.
Experience working on large-scale ecommerce marketplace or consumer platforms with strong data and ML components.

Additional Details

This job posting relates to an existing vacancy within eBay.

eBay is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race color religion national origin sex sexual orientation gender identity and disability or other legally protected you have a need that requires accommodation please contact us at. We will make every effort to respond to your request for accommodation as soon as possible. View our accessibility statement to learn more about eBays commitment to ensuring digital accessibility.

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Required Experience:

Manager

Join a team of passionate thinkers innovators and dreamers and help us connect people and build communities to create economic opportunity for all.

About the team and the role

What you will accomplish

**AIfirst.** You treat agentic AI and modern ML/LLM systems as a core design and operating lever using them as a force multiplier for engineering productivity model quality and customer impact while setting clear guardrails for responsible AI safety and ethics.
**Generative leadership.** You lead with outcomes over outputs optimize flow (shorter lead times limited WIP fast feedback) create psychological safety for intelligent failure invite rather than inflict change and practice servant leadership grounded in continuous experimentation and improvement.
Lead and grow a high-performing team of 812 building seller-facing data and intelligence products.
Stay actively involved in system design model architecture reviews and hands-on technical contribution across data pipelines serving systems and ML/LLM workflows (Java/Kotlin Python Spark/Scala or similar).
Own the ML and applied AI roadmap for the team including classical ML models ranking and recommendation systems embeddings and LLM-based understanding for seller insights pricing sell-through and product research.
Drive best practices in data engineering and ML systems including data quality lineage reproducibility model lifecycle (training evaluation deployment monitoring) and responsible AI.
Provide technical leadership through mentorship design and code reviews model reviews and architectural guidance across backend data ML and research workstreams.
Own and drive architectural decisions for distributed data systems and ML serving with strong reasoning about consistency freshness latency cost and scale trade-offs.
Partner closely with Product Design Data Science and Engineering and partner engineering teams to align on goals prioritization and execution.
Foster a strong engineering and applied research culture centered on ownership continuous improvement scientific rigor and technical excellence.

What you will bring

BS in Computer Science Engineering or equivalent technical degree.
12 years of software engineering experience with deep expertise across data and/or ML systems including:
Object-Oriented Programming (OOP) and SOLID principles.
Designing and operating large-scale data pipelines (batch and streaming ETL) on platforms such as Spark Hadoop Airflow or equivalents.
Hands-on experience with multiple storage paradigms search engines (OpenSearch / Elasticsearch) document databases key-value stores and relational/analytical stores and the trade-offs between them.
Designing and building distributed systems with strong understanding of consistency durability freshness and availability trade-offs.
Scalable API and service design (GQL REST and service-oriented architectures) for data- and ML-backed products.
4 years of experience managing engineering teams that include service ML engineers data engineers and/or applied researchers building strong teams and handling performance and growth with empathy and clarity.
Solid working knowledge of the modern ML lifecycle feature engineering training evaluation deployment monitoring and retraining and of how ML and LLM-driven capabilities are productized into customer-facing experiences.
Strong hands-on technical foundation this role requires active technical contribution in design reviews and prototyping.
Excellent knowledge of software design principles data architecture and ML system design.
Experience working in Agile environments (Scrum Kanban) including sprint planning roadmap delivery and iterative experimentation.
Proficiency with tools like Jira (or equivalent) for backlog management and delivery tracking.
Strong planning and prioritization skills including breaking down complex data and ML initiatives into manageable measurable milestones.
Ability to provide accurate estimations and balance delivery speed model quality and operational cost.
Experience translating product and business requirements into technical and modeling designs and execution plans.
Excellent communication skills with the ability to work effectively across engineering research product and business stakeholders.
Strong problem-solving skills with the ability to proactively identify and mitigate risks dependencies and data/model regressions.

## Bonus Skills

Experience building seller- merchant- or marketplace-facing data and intelligence products (pricing demand recommendations business analytics).
Hands-on experience with LLMs and applied GenAI RAG architectures embeddings fine-tuning evaluation and agentic workflows in production.
Experience with vector databases and hybrid (lexical semantic) search at scale.
Familiarity with feature stores ML platforms (e.g. MLflow Kubeflow SageMaker or equivalents) and model serving frameworks.
Experience with cloud-native architectures Kubernetes Docker and containerized deployments.
Experience with CI/CD pipelines data observability and ML monitoring (data drift model performance cost).
Understanding of distributed system observability (logging tracing metrics) for both services and ML pipelines.
Experience working on large-scale ecommerce marketplace or consumer platforms with strong data and ML components.

Additional Details

This job posting relates to an existing vacancy within eBay.

Required Experience:

Manager

Apply Now

About Company

EBay

Founded in 1995 in San Jose, Calif., eBay (NASDAQ: EBAY) is where the world goes to shop, sell and give. Whether you’re buying new or used, common or luxurious, trendy or rare – if it exists in the world, it’s probably for sale on eBay. Our great value and unique selection help every ... View more

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