Develop and execute a comprehensive lifecycle roadmap across email, SMS, and push channels to drive user activation and retention. Build scalable templates and personalized, event-based messaging to turn new signups into habitual users.
Wizard
6 Remote Job Openings at Wizard
Define and evolve accuracy metrics and evaluation frameworks to measure the performance of the AI shopping agent. Lead the science work to improve agent quality through LLM judge fine-tuning and rigorous experimentation.
Design and build scalable backend services that integrate LLMs and ML models into production-ready AI product experiences. Collaborate with cross-functional teams to maintain data pipelines and ensure high system performance, reliability, and observability.
The Senior MLOps Engineer will own the end-to-end ML lifecycle, including model packaging, deployment, monitoring, and optimization for a custom inference platform powering a conversational shopping agent. Responsibilities include building and optimizing production-grade ML pipelines and defining strategies for model versioning, rollout, and lifecycle management.
The engineer will be responsible for building and productionizing feedback loops to continuously improve the AI agent's performance over time. This includes owning signal pipelines end-to-end, building evaluation infrastructure, and designing appropriate learning approaches.
Lead the roadmap for conversational and agentic AI features across various platforms. Collaborate with cross-functional teams to define AI agent behaviors and improve user engagement.
