Technical Product Lead, Voice AI Research Team

Define and Own the Quality Bar: Partner with engineering to establish Regal's point of view on what makes the best Voice AI agent—across naturalness, accuracy, task completion, and conversational flow. Translate that vision into measurable standards the entire organization rallies around.

Optimize the Latency-Cost-Intelligence Tradeoff: Develop the product strategy for balancing response time, inference cost, and model capability. Partner with engineering to architect a pipeline where customers get the smartest, fastest responses at sustainable unit economics.

Build Evaluation & Benchmarking Frameworks: Develop comprehensive eval sets, automated regression suites, and A/B testing infrastructure to ensure Regal's agents stay top-performing as models, prompts, and configurations evolve.

Stay at the Frontier: Track emerging trends in LLMs, speech-to-text, text-to-speech, and real-time inference. Evaluate new models, techniques, and architectures and translate the ones that matter into Regal's production stack.

Drive Competitive Differentiation: Develop and execute a strategy for how Regal's Voice AI agents outperform alternatives—whether through model selection, latency optimization, or proprietary fine-tuning.

Bridge Customers and Infrastructure: Go deep with customers to understand what "quality" means in their specific context (industry, use case, caller expectations), then translate those insights into infrastructure and model-level improvements that scale across the platform.

Measure, Iterate, Ship: Define success metrics for agent quality (e.g., conversation completion rate, caller sentiment, response latency percentiles, escalation rate). Build dashboards, run experiments, and drive a continuous improvement loop with engineering and applied AI.

Technical Depth with Product Instinct: 4–6 years of experience as a Product Manager, Product Engineer or founder, ideally working on AI/ML products, infrastructure, or developer platforms. This role is Product lead for a research team, so you're comfortable navigating model architectures, inference pipelines, and API design—and you know how to translate that understanding into product decisions that move metrics. 

Eval & Data Mindset: You think in experiments, benchmarks, and feedback loops. You've built or contributed to evaluation frameworks, A/B testing programs, or quality scoring systems—and you understand why rigorous measurement is the foundation of any AI product.

Natural Leader: You set a vision and bring others along. You communicate complex tradeoffs crisply, make decisions with incomplete information, and create clarity for your team in ambiguous, fast-moving environments.

Deeply Curious, High Agency: You don't wait to be told what to investigate. You dig into call recordings, run your own experiments, read research papers, and chase down the root cause. You act with urgency and ownership.