Technical Product Lead, Voice AI Research Team

Regal•New York, NY

13h•Hybrid

About The Position

As a Technical Product Lead on the Voice AI Agent team, you'll drive the experience that matters most: making Regal's voice AI agents indistinguishable from the best human agents and the highest-performing in the market. You'll partner closely with engineering to set the product vision for how Regal evaluates, benchmarks, and continuously improves agent quality—building the eval frameworks, defining the performance bar, and translating frontier AI research into production gains our customers can feel on every call. This role sits at the heart of what makes Regal win. You'll work across infrastructure and applied to drive decisions that directly impact conversation quality and competitive differentiation. If you want to shape what "best-in-class" means for Voice AI—and have the technical depth and product instinct to back it up—this role is for you.

Requirements

4–6 years of experience as a Product Manager, Product Engineer or founder, ideally working on AI/ML products, infrastructure, or developer platforms.
Comfortable navigating model architectures, inference pipelines, and API design—and know how to translate that understanding into product decisions that move metrics.
Think in experiments, benchmarks, and feedback loops.
Have built or contributed to evaluation frameworks, A/B testing programs, or quality scoring systems—and understand why rigorous measurement is the foundation of any AI product.
Set a vision and bring others along.
Communicate complex tradeoffs crisply, make decisions with incomplete information, and create clarity for your team in ambiguous, fast-moving environments.
Don't wait to be told what to investigate. Dig into call recordings, run your own experiments, read research papers, and chase down the root cause.
Act with urgency and ownership.

Nice To Haves

Computer Science, Math, or Engineering background preferred but not required. What matters is that you can go deep on technical problems and come back with a clear product direction.

Responsibilities

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.
Align and Communicate: Set a clear vision for the Voice AI team and communicate it crisply to leadership, engineering, and cross-functional stakeholders. Manage the roadmap, make hard prioritization calls, and keep the team moving fast toward outcomes that matter.