Klaviyo is looking to build state-of-the-art AI and machine learning technologies to power its products and develop AI agents that can automatically create and execute marketing or customer experiences, strategies, and campaigns for businesses. The Sr. AI Engineer will play a key role in designing and building scalable backend systems and user experiences for these AI products and AI agent solutions.
Requirements
- Have hands-on experience building and deploying generative AI and agentic AI applications into production, with expertise in prompt engineering, few-shot learning, fine tuning and evaluation.
- Experienced backend engineer with a strong track record of building scalable, distributed systems, especially in the service of AI agent capabilities.
- Proficient in Python and modern backend frameworks (FastAPI, Django preferred).
- Experience creating human and automated evals to ensure high AI model quality.
- Proficient in big data tools such as Apache Spark and Hadoop.
- Deep experience with asynchronous processing and distributed task queues (Celery, Kafka, SQS, RabbitMQ, Redis).
- Strong understanding of database technologies and ORMs (SQLAlchemy, Alembic).
Responsibilities
- Design and build backend systems that support scaling our AI solutions for 167K+ customers.
- Develop robust, reliable and scalable data collection and processing pipelines for machine learning models to train and consume.
- Develop robust, reliable and scalable services to serve AI models in production environments.
- Contribute to evolving our agentic architecture — making our AI agents more self-sufficient and performant.
- Contribute to a culture of ownership, experimentation, and customer-centric product thinking.
Other
- 5–7 years of professional experience in software engineering, with a strong focus on backend systems and distributed architectures.
- Able to operate with a high degree of autonomy, handle ambiguity, and thrive in a fast-moving, startup-like environment.
- Driven by curiosity. You're someone who learns and stays up to date with this rapidly evolving field.
- Comfortable collaborating directly with product managers and customers to shape solutions.
- Trained ML models in the past and deployed them in production systems to generate impact to businesses.
