AI Applied Scientist

Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust. We are the first AI Agent purpose-built for ecommerce, transforming how consumers shop in the agentic era.

We are hiring an AI Applied Scientist to make our shopping agent's performance visible, trusted, and continuously improving. This is a foundational science hire focused on evaluation infrastructure. The position bridges applied ML, evaluation science, and product to establish how the shopping agent measures and improves accuracy. We want to bring scientific rigor to the most important question at Wizard: is our agent getting better, and how do we know?

What you'll do:

• Define and evolve accuracy metrics across the full shopping experience (retrieval, ranking, recommendations, outcomes).
• Design and run experiments to measure improvements and regressions.
• Build and maintain evaluation datasets, benchmarks, and scoring frameworks.
• Improve the LLM judges that power our evaluation pipeline: prompting, calibration, and fine-tuning.
• Identify failure modes and edge cases, and drive improvements through data.
• Partner with ML/AI Engineering on model validation.
• Make agent performance visible, trusted, and actionable across product and engineering.

What we're looking for:

• 5+ years in Applied ML, AI Research, or Applied Science (PhD or equivalent depth strongly preferred).
• Hands-on experience evaluating modern AI/ML systems: LLMs, agents, ranking, or recommendations.
• Direct experience with LLM-based systems: judge models, RAG, prompt engineering, fine-tuning, RLHF.
• Strong experimentation and statistical rigor.
• Ability to operate in ambiguity and influence cross-functional teams.

Compensation: $225,000 - $280,000 USD base. Benefits include medical, dental, and vision coverage; 401(k); equity (stock options); flexible PTO; fully remote within the US; and periodic offsites.