Senior Software Engineer - Product Recommendations
At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements.
What this role actually needs.
At Klaviyo, we value the unique backgrounds, experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. Responsibilities: - Lead the design, architecture, and operation of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), upholding standards for reliability, performance, and clear APIs. - Architect and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catalog data into high-quality features and inputs for recommendation models, ensuring data quality and lineage. - Collaborate closely with ML engineers and product stakeholders to strategically productionize recommendation models —defining high-level interfaces, robust feature contracts, and advanced deployment patterns for batch and/or real-time inference systems. - Drive the development of ML/AI systems such as vector search that power recommendation, semantic search, and sophisticated agentic use cases. - Implement and evolve data and service observability (metrics, logging, tracing, dashboards) to proactively ensure recommendations are correct, fast, and highly available for all customers. - Contribute to and mentor others on shared data frameworks, libraries, and architectural patterns to accelerate the development of new recommendation use cases and iteration velocity across the team. Requirements: - Lead the design, architecture, and operation of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), upholding standards for reliability, performance, and clear APIs. - Architect and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catalog data into high-quality features and inputs for recommendation models, ensuring data quality and lineage. - Collaborate closely with ML engineers and product stakeholders to strategically productionize recommendation models —defining high-level interfaces, robust feature contracts, and advanced deployment patterns for batch and/or real-time inference systems. - Drive the development of ML/AI systems such as vector search that power recommendation, semantic search, and sophisticated agentic use cases. - Implement and evolve data and service observability (metrics, logging, tracing, dashboards) to proactively ensure recommendations are correct, fast, and highly available for all customers. - Contribute to and mentor others on shared data frameworks, libraries, and architectural patterns to accelerate the development of new recommendation use cases and iteration velocity across the team. Company context: Klaviyo is the public marketing automation and CDP company powering email, SMS, and AI personalization for ecommerce.
Day-to-day expectations
Klaviyo lists these responsibilities for the Senior Software Engineer - Product Recommendations role.
- Lead the design, architecture, and operation of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), upholding standards for reliability, performance, and clear APIs.
- Architect and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catalog data into high-quality features and inputs for recommendation models, ensuring data quality and lineage.
- Collaborate closely with ML engineers and product stakeholders to strategically productionize recommendation models —defining high-level interfaces, robust feature contracts, and advanced deployment patterns for batch and/or real-time inference systems.
- Drive the development of ML/AI systems such as vector search that power recommendation, semantic search, and sophisticated agentic use cases.
- Implement and evolve data and service observability (metrics, logging, tracing, dashboards) to proactively ensure recommendations are correct, fast, and highly available for all customers.
- Contribute to and mentor others on shared data frameworks, libraries, and architectural patterns to accelerate the development of new recommendation use cases and iteration velocity across the team.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Lead the design, architecture, and operation of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), upholding standards for reliability, performance, and clear APIs.
- Architect and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catalog data into high-quality features and inputs for recommendation models, ensuring data quality and lineage.
- Collaborate closely with ML engineers and product stakeholders to strategically productionize recommendation models —defining high-level interfaces, robust feature contracts, and advanced deployment patterns for batch and/or real-time inference systems.
- Drive the development of ML/AI systems such as vector search that power recommendation, semantic search, and sophisticated agentic use cases.
- Implement and evolve data and service observability (metrics, logging, tracing, dashboards) to proactively ensure recommendations are correct, fast, and highly available for all customers.
- Contribute to and mentor others on shared data frameworks, libraries, and architectural patterns to accelerate the development of new recommendation use cases and iteration velocity across the team.
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Source: klaviyo.com · Source ID: 7660745003 · Confidence: 88/100 · Last checked: May 7, 2026
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