- Own end-to-end and integration-level model evaluation across accuracy, latency, and feature-specific metrics (e.g., turn detection latency, endpointing accuracy)
- Build and maintain competitive benchmarking pipelines against other providers in the market
- Design and run systematic experiments to measure the impact of model changes
- Onboard, curate, and maintain evaluation datasets—both public benchmarks and internal test sets
- Create evaluation subsets that stress-test specific capabilities and edge cases
- Define evaluation metrics that capture real-world performance
- Translate qualitative customer feedback into quantifiable evaluation criteria
- Work with customer-facing teams to understand pain points and convert them into research priorities
- Reduce friction for researchers by maintaining clean evaluation pipelines and clear documentation
- Identify evaluation gaps proactively and propose solutions
- Move fast—iterate on benchmarking approaches weekly, not monthly
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