Sr Engineering Manager - Data and ML

eBay•Toronto, ON

About The Position

We are looking for a Senior Engineering Manager to lead a multidisciplinary team within eBay's 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.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.
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 8–12 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.

Benefits

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 status.
If you have a need that requires accommodation, please contact us at [email protected]. We will make every effort to respond to your request for accommodation as soon as possible.
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