Lead Artificial Intelligence Engineer

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

What the job involves 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

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