Software Engineer II - Recommendations

Role

Klaviyo is hiring a Software Engineer II for its Recommendations Platform Team in Boston, MA. This role is onsite five days per week.

The team develops and deploys machine learning-based recommendation systems at scale and builds foundations for embedding-based similarity search and agentic workflows. Its systems cover large-scale data pipelines, querying, batch training and inference, low-latency retrieval and ranking, and experimentation to measure recommendation quality and business impact.

Responsibilities

• Contribute to the architecture and evolution of reliable, performant backend services and clear APIs that power recommendations across email, SMS, KAgent, onsite experiences, and other channels.
• Build and maintain large-scale data processing pipelines that transform events and catalog data into model features and inputs, ensuring data quality and lineage.
• Collaborate with ML engineers and product stakeholders to productionize models, including interfaces, feature contracts, and deployment patterns for batch and real-time inference.
• Contribute to the vector database supporting recommendations, semantic search, and agentic use cases.
• Implement metrics, logging, tracing, and dashboards to support correct, explainable, fast, and highly available recommendations.
• Work with Product to define milestones and balance experimentation with technical soundness and maintainability.
• Lead data-driven decisions and A/B testing, instrument systems, and independently interpret results to guide iterations.
• Participate in on-call and incident response and drive substantial post-incident improvements.
• Integrate AI into development workflows, testing, monitoring, and debugging.
• Share knowledge, mentor junior engineers, and establish practices for large-scale data frameworks, distributed systems, and production ML integration.

Qualifications

• At least two years of professional software engineering experience focused on backend and distributed systems at scale, including production services and optimization for latency, reliability, operability, and business needs.
• Proficiency in Python and openness to other languages.
• Comfort with cloud-native architectures, container orchestration such as Kubernetes, infrastructure management, and CI/CD pipelines. AWS is preferred.
• Experience with data-driven decisions and A/B testing, including defining experiments or an interest in learning instrumentation, interpreting results, and incorporating findings into system design.
• Comfort designing and querying data models across relational, analytical, and NoSQL datastores, such as Postgres, MySQL, data warehouses, Redis, and vector databases.
• Familiarity with modern DevOps practices, including CI/CD, monitoring, and alerting, applied to large-scale data and recommendation systems.
• A track record of owning features from design and implementation through rollout, monitoring, and iteration.
• Excellent technical collaboration and communication, including explaining trade-offs to technical and nontechnical partners and aligning cross-functional teams.
• A self-starting approach, hands-on experimentation with AI in work or personal projects, and enthusiasm for responsibly developing AI tools and workflows.

Nice to Have

• Experience with recommendation systems or adjacent ranking, personalization, search, or ML-powered features.
• Experience with big-data frameworks such as Apache Spark, Flink, or Beam for complex batch or streaming pipelines.
• Experience integrating ML models into production, building ML-powered features, or contributing to ML infrastructure.
• Experience training and iterating on models for ranking, prediction, or personalization.
• Experience with Ray or similar ML and distributed-compute frameworks.
• Experience partnering with data science or ML teams on feature stores, offline/online parity, advanced model deployment, and monitoring.
• Background in e-commerce, marketing technology, or consumer personalization.

Compensation and Travel

• US base salary range: $116,000–$174,000 USD. Actual pay depends on job-related skills, experience, education or training, and location.
• Eligible total compensation may include an annual cash bonus, equity, sign-on payments, and health, welfare, and wellbeing benefits.
• Up to 10% travel may be required for onboarding, client or partner work, team meetings, and industry events. Travel is coordinated in advance.

Application and Interview Information

• Candidates who are close but not exact matches are encouraged to apply.
• Responsible AI use includes privacy, security, bias awareness, and human oversight. Accommodations are available as needed.
• Interview participants must read and adhere to Klaviyo's guidelines for AI use during interviews.
• Submitting an application consents to personal-data processing under Klaviyo's Job Applicant Privacy Notice.
• Klaviyo will never request payment, bank details, or personal financial information during the application process. Legitimate postings appear on its official careers site; applicants should be cautious of unsolicited offers or messages from outside the company's email domain.
• Massachusetts law prohibits requiring or administering a lie detector test as a condition of employment or continued employment.