Software Engineer, Conversational Products & Agent Workflows

Klaviyo • Boston, MA

15h • $116,000 - $174,000

About The Position

Klaviyo is seeking a Software Engineer to join the Customer Agent team, its AI-native conversational platform. The role focuses on building scalable backend systems and AI-powered product experiences that enable agents to retrieve context, use tools, and take reliable action on behalf of customers. It is backend-heavy, with opportunities to contribute to user-facing experiences. The engineer will independently own meaningful parts of larger systems, contribute to technical decisions, and deepen expertise in backend engineering, applied AI, and production reliability.

The Customer Agent team builds intelligent systems that can take action, learn from outcomes, and improve customer experiences. Klaviyo serves more than 167,000 customers and processes billions of consumer profiles, messages, interactions, and conversion signals, providing opportunities to build AI systems for customer experiences at scale.

The role includes developing agentic features and workflows, addressing applied AI and evaluation challenges, improving reliability, building asynchronous and distributed processing, participating in on-call, and working with customers and cross-functional partners to improve shipped products. The position may require up to 10% travel, coordinated in advance.

Requirements

• At least 3 years of professional software engineering experience, including experience building backend systems or distributed applications.
• Hands-on experience building or contributing to generative AI or agentic AI applications, ideally used by real users or in production environments.
• Proficiency in Python and experience with modern backend frameworks such as FastAPI or Django.
• Experience with asynchronous processing or distributed task/event systems such as Celery, Kafka, SQS, RabbitMQ, or Redis.
• Working knowledge of databases, data modeling, APIs, and persistence patterns used in production systems.
• Comfort working in cloud environments and experience with technologies such as AWS, containers, Kubernetes, infrastructure automation, or CI/CD systems.
• Ability to reason about tradeoffs among quality, latency, cost, reliability, and implementation complexity, and to seek additional technical context when needed.
• Comfort working through ambiguity, breaking larger problems into smaller pieces, and making progress without fully specified requirements.
• Experience with, or strong interest in, working directly with external customers to understand problems and improve products.
• Interest in shipping useful, reliable products and deepening engineering judgment in a rapidly evolving technical space.

Nice To Haves

• Experience training, fine-tuning, distilling, or otherwise adapting machine learning or language models.
• Experience with reinforcement learning or feedback-driven optimization.
• Experience with evaluation infrastructure such as LLM-as-judge systems, evaluator calibration, benchmark datasets, or AI quality tooling.
• Experience operating AI systems at meaningful production scale or optimizing inference cost, latency, or throughput.

Responsibilities

• Build and improve reliable backend systems and APIs for AI-driven customer experiences.
• Develop production agentic features involving tool use, context management, retrieval, structured outputs, orchestration, and multi-step workflows.
• Address applied AI problems involving retrieval and RAG, grounding, hallucination mitigation, model selection, and LLM-based, deterministic, or hybrid approaches.
• Build and use evaluation approaches, including representative datasets, automated and human evaluation, regression testing, qualitative error analysis, and production signals.
• Improve system reliability through guardrails, retries, fallbacks, observability, monitoring, and thoughtful failure handling.
• Build asynchronous and distributed processing workflows that support AI workloads at scale.
• Participate in an on-call rotation and help diagnose, mitigate, and learn from production incidents.
• Work with external customers and cross-functional partners to understand workflows, identify pain points, and translate feedback into product improvements.
• Monitor shipped experiences and use quality metrics, system performance, and customer feedback to guide iteration.

Benefits

• The total compensation package may include participation in the company's annual cash bonus plan, equity, and sign-on payments.
• Comprehensive health, welfare, and wellbeing benefits based on eligibility.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed