Software Engineer II - Recommendations
$116,000–$174,000 year
On-siteBoston, Massachusetts, United States
Job Summary
Contribute to the architecture and evolution of backend services powering product recommendations across Klaviyo experiences, ensuring reliability, performance, and clear APIs. Maintain robust, large-scale data processing pipelines using Apache Spark or similar frameworks to transform raw events into high-quality features for recommendation models. Collaborate with ML engineers and product stakeholders to productionize recommendation models, defining interfaces and deployment patterns for batch and real-time inference systems. Ensure data and service observability through metrics, logging, and tracing to facilitate correct, explainable, and highly available recommendations. Lead data-driven decision making and A/B testing efforts, independently interpreting results to guide future product and engineering iterations. Participate in on-call and incident response for owned systems, driving post-incident follow-ups to improve resilience. Integrate AI into the development workflow to accelerate development, automate tests, or build smarter monitoring tools. Share knowledge and mentor junior engineers on working with large-scale data frameworks and distributed systems.
Required Qualifications
- 2+ years of professional software engineering experience with a focus on backend and distributed systems at scale
- Proven track record working on production services and optimizing for latency, reliability, and operability as well as business requirements
- Proficient in Python
- Comfortable with cloud-native architectures (AWS preferred)
- Comfortable with container orchestration (e.g., Kubernetes)
- Experience managing infrastructure and CI/CD pipelines as a core part of your development process
- Experience in data-driven decision making and A/B testing
- Ability to define how to instrument experiments, read and interpret results, and ensure learnings are folded back into system design
- Comfortable designing and querying data models in relational, analytical, and NoSQL datastores
- Experience with Postgres, MySQL, data warehouses, Redis, or vector databases
- Feel at home with modern DevOps practices (CI/CD, monitoring, alerting)
- Track record of owning features end-to-end—from initial technical design and implementation through rollout, monitoring, and sustained iteration
- Excellent technical collaborator and communicator
- Ability to clearly articulate complex technical trade-offs to both technical peers and non-technical partners
- Ability to work effectively to drive alignment across ML Engineers, Software Engineers, PMs, and other teams
- Self-starter who has actively experimented with AI in work or personal projects
- Excitement to responsibly explore and define new AI tools and workflows to enhance team productivity and system intelligence
- Must be available for Boston, MA onsite 5x a week
- Must be able to travel up to 10% for purposes such as new hire onboarding, client or partner work if applicable, team meetings, and industry events
Desired Qualifications
- Previous experience working on product recommendation systems or adjacent ML-powered features (ranking, personalization, search, or similar)
- Experience with big data frameworks such as Apache Spark (or similar technologies like Flink, Beam, etc.) for architecting and building complex batch or streaming pipelines
- Experience in AI/ML systems and products, such as integrating models into production systems, building features powered by ML, or contributing to the ML infrastructure
- Experience training and iterating on machine learning models (e.g., for ranking, prediction, or personalization)
- Experience with ML and distributed compute frameworks such as Ray or similar tools
- Experience partnering with data science or ML teams to productionize models (designing feature stores, ensuring offline/online parity, advanced model deployment and monitoring)
- Background in e-commerce, marketing tech, or consumer personalization products
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