AI Applied Scientist

About Wizard

Wizard is the top-performing AI Shopping Agent, delivering the best products from across the web with unmatched accuracy, quality, and trust.

The Role

We’re looking for an Applied Scientist to own how we measure, understand, and improve the accuracy of our AI agent. This role sits at the intersection of applied ML, evaluation science, and product. You’ll define what “good” looks like for our agent, build the systems to measure it, and lead the science work to improve it, including fine-tuning the LLM judges that power our evaluation pipeline.

You’ll partner with ML Engineering and AI Engineering. What you will do is bring scientific rigor to the most important question at Wizard: is our agent getting better, and how do we know?

This is a foundational hire on our science team. Evaluation is the starting point, and the role is scoped to grow into broader applied science work as the surface area of the agent expands (recommendations, personalization, ranking, multimodal, conversational understanding).

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 where it matters
• Translate ambiguous product questions into clear, measurable hypotheses and analysis
• Partner with ML Engineers to validate model changes and guide iteration
• Identify failure modes and edge cases, and drive improvements through data
• Make agent performance visible, trusted, and actionable across product and engineering

First 3 months

• Go deep on the agent, the current eval pipeline, and the metrics we use today
• Audit existing accuracy metrics and benchmarks; identify gaps, blind spots, and signals that aren’t trustworthy
• Build relationships with ML, AI Engineering, and Product
• Ship one quick win: a missing benchmark, an improved metric, or a fix to a misleading signal
• Establish a baseline view of agent performance the team can rally around

Months 3 to 6

• Own the evaluation framework: datasets, metrics, scoring, reporting, both offline and online
• Drive measurable improvements to LLM judge quality (calibration, fine-tuning where appropriate)
• Run experiments that influence at least one significant model or product change
• Stand up automated evaluation the team trusts before and after every launch
• Build dashboards and reporting that make agent performance legible to leadership

Beyond 6 months

• Lead applied science work on the next frontier as the agent grows: multi-turn evaluation, multimodal, personalization, ranking quality, conversational understanding
• Influence team-level strategy on what we measure, what we improve, and why
• Mentor and help grow the science function as it expands

What Success Looks Like

• Clear, trusted accuracy metrics are consistently used across product and engineering
• A robust automated evaluation framework for both offline and live experiments
• Model and product changes are consistently measured before and after launch
• Demonstrable improvements in LLM judge quality and eval coverage
• Science leadership that informs what we build, not just whether it works

Career Growth

• Depth track: become the org’s authority on AI evaluation: eval strategy, judge models, agent benchmarking
• Breadth track: expand into other applied science problems (recommenda