Lead AI Engineer

Job Title: Lead AI Engineer – Marketing & Service AI
Location: Palo Alto CA

This role is based in our new Palo Alto hub. We operate a hub‑first model with regular in‑office collaboration expected.

At Klaviyo we believe the future of software isn’t just better productivity tools for humans—it’s systems that can run optimize and adapt themselves based on outcome and reward signals. We’ve already built the infrastructure and applications that sit between businesses and consumers—serving 183000+ customers billions of consumer profiles and hundreds of billions of messages and conversion events. Now we’re investing heavily in Marketing AI and Service AI: the shared services and agentic capabilities that power AI‑native experiences across Klaviyo.

As a Lead AI Engineer in the AI & Analytics organization you’ll play a key role in designing and building scalable backend systems and user experiences that power our AI products and agentic solutions. You’ll own complex services end‑to‑end contribute to architecture for high‑impact AI features and partner closely with product managers machine learning engineers and data scientists to turn AI ideas into reliable scalable production capabilities. This is a hands‑on backend‑heavy role with opportunities to influence architecture async processing pipelines distributed systems and more—without being the overall area tech lead.

You’ll be based in our new Palo Alto office a center of gravity for AI at Klaviyo—surrounded by teammates with deep ML and AI experience and tightly connected to our other U.S. and international R&D hubs. You’ll collaborate daily with engineers in Boston and other locations and you’ll have a clear path to grow into broader technical leadership staff/principal scope or future people leadership if that’s the direction you want to go—without being constrained by hub location.

• Design and build core AI services. Implement scalable low‑latency backend systems and APIs that power our Marketing and Service AI capabilities for 183K+ customers handling billions of events and interactions.
• Scale AI data and inference pipelines. Develop robust reliable and scalable data collection and processing pipelines so our generative and agentic models have the features context and feedback they need to perform well in production.
• Build and harden AI serving systems. Implement services that host and orchestrate AI models (LLMs tools evaluators retrieval systems) with strong contracts logging and observability so other teams can depend on them.
• Evolve our agentic architecture. Help evolve how our agents plan call tools and react to feedback—shipping improvements that increase autonomy reliability and safety for both customer‑facing and internal workflows.
• Apply and refine best practices for AI systems. Use and help improve standards for evaluation safety/guardrails prompt and model management offline/online tests and incident response for AI‑backed systems.
• Collaborate across teams. Work closely with product ML data and platform teams to clarify requirements align on interfaces and unblock dependencies for the services you own—especially across hubs and time zones.
• Mentor and uplevel teammates. Provide thoughtful code reviews share patterns and examples and help mid-level and junior engineers grow stronger in building distributed and AI‑powered systems.
• Help shape the Palo Alto hub. Participate in interviewing onboarding and local engineering rituals; contribute ideas that make Palo Alto a highly collaborative high‑bar hub that works seamlessly with other locations.
• Measure what matters. Instrument your services and use metrics—availability latency cost‑to‑serve agent success rates eval scores and customer adoption—to guide your own technical decisions and influence the team roadmap.
• You’ve already been effectively practicing agentic coding in your daily work. You’re hungry to responsibly explore new AI tools and workflows finding ways to make your work smarter and more efficient.

• Experienced backend engineer. 5–7+ years of professional software engineering experience with a strong focus on backend and distributed systems; you’ve led complex projects end‑to‑end within a team and owned services in production.
• Hands‑on with generative & agentic AI in production. You’ve built and shipped generative or agentic AI applications (e.g. LLM‑backed flows tool‑using agents retrieval‑augmented systems) and are comfortable with prompt design few‑shot approaches fine‑tuning and evaluation.
• Strong distributed systems and async background. You’ve built reliable services async processing pipelines and distributed task queues (e.g. Celery Kafka SQS RabbitMQ Redis) that support high‑throughput workloads.
• Fluent in Python and data tooling. Proficient in Python and modern backend frameworks (FastAPI Django or similar) with experience using big data tools such as Spark/Hadoop and ORMs like SQLAlchemy/Alembic.
• Comfortable in cloud‑native environments. Experience with AWS and Kubernetes CI/CD pipelines observability and operational best practices; you understand how infrastructure choices affect reliability latency and cost.
• Evaluation and quality‑minded. You’ve created human and/or automated evals for AI systems instrumented for quality and understand how to balance latency cost and response quality in real‑world usage.
• Collaborative and customer‑first. You enjoy working directly with PMs engineers in other teams and sometimes customers; you care deeply about the customer experience and use data and feedback to guide decisions.
• Growth‑oriented teammate. You communicate clearly give and receive feedback well and contribute to a team culture that is humble ambitious and values‑aligned.
• You’ve already experimented with AI in work or personal projects and you’re excited to dive in and learn fast. You’re hungry to responsibly explore new AI tools and workflows finding ways to make your work smarter and more efficient.
• You’ve already been effectively practicing agentic coding in your daily work—or you’re eager to deepen that practice—using AI assistants and tools to scaffold code run experiments and iterate faster while keeping yourself in the driver’s seat.
• Bonus: You use AI tools to accelerate exploration shorten iteration cycles and bring sharper ideas to the table—for yourself and your teammates.

• Prior experience training and deploying your own ML models (including RL or RLHF) into production systems that drove measurable business impact.
• Experience building shared AI/ML platforms or services used by multiple product teams.
• Background in marketing tech ecommerce or other domains where customer data personalization and experimentation are central.

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What We Do

Klaviyo (NYSE: KVYO) is the B2C CRM. Powered by its built-in data platform and AI Klaviyo combines marketing automation analytics and customer service into one unified solution making it easy for businesses to know their customers and grow faster. Klaviyo (CLAY-vee-oh) helps over 183000 brands like Mattel Glossier Daily Harvest and Liquid Death deliver 1:1 experiences at scale improve efficiency and drive revenue.

Why Work With Us

We refer to our employees as ‘Klaviyos’ and we make up a diverse community united around shared values: We’re curious collaborative driven innovative fun and fully ourselves at work. No matter which team you join your work won’t just impact Klaviyo. It’ll help empower our customers and enable creators across the globe to own their destinies.

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