Director of Voice AI

Motorola Solutions•New York, MA

4d•$220,000 - $270,000•Remote

About The Position

Motorola Solutions is seeking a Director of Voice AI to lead a unified, world-class research and engineering organization responsible for the end-to-end lifecycle of Voice AI technologies. This executive leader will be responsible for turning ambiguous concepts into enterprise-scale Voice AI solutions, defining the technical vision, bridging the gap between AI research and product engineering, and ensuring the delivery of next-generation voice intelligence solutions across Motorola’s product portfolio. The role reports to the VP of AI for Motorola Solutions.

Requirements

Bachelors degree with 10+ years of technical leadership experience leading Voice AI and Speech Technology teams and organizations.
5+ years of hands-on experience building and optimizing Voice AI models (e.g., ASR/NLU/TTS systems) or as a Voice AI researcher.
Deep, demonstrable expertise in Voice AI (e.g., ASR, NLU, LLMs), machine learning algorithms, and the end-to-end MLOps lifecycle.
Proven ability to engage in complex technical discussions, define the architectural vision for central, reusable AI infrastructure and models, and drive technical strategy while managing critical trade-offs.
Proven experience defining long-term technical vision, engineering strategy, and roadmaps for a large-scale platform.
Expertise in defining and implementing technical metrics (e.g., latency, throughput, system reliability, model efficiency) to measure engineering excellence and drive continuous improvement across an organization.
Demonstrated ability to manage and scale a unified, distributed engineering and research organization of 30 to 50 people, mentor senior technical talent, and lead multiple teams across different geographies.
Hands-on experience driving innovation through foundational research, working with AI performance metrics (e.g., Precision/Recall), real-time audio/speech processing, and inference optimization.
Track record of translating highly ambiguous product concepts and complex customer needs into clear, executable technical roadmaps and architectural specifications.
Experience in a start-up or fast-paced environment is highly valued, demonstrating high ownership, bias for action, comfort with ambiguity, and the ability to drive 0-to-1 product development with limited resources.

Nice To Haves

Experience in public safety, security-focused software (e.g., speech analytics, real-time dispatch management), mission-critical systems, or emergency communications.
Experience building enterprise-scale voice assistants and/or AI agents.
Deep understanding of hybrid cloud/edge architecture, including the trade-offs for deploying voice AI models on edge devices versus cloud environments.

Responsibilities

Drive the technical vision, research and engineering execution for the Voice AI platform.
Define the long-term research agenda for fundamental Voice AI and speech capabilities and drive a culture of scientific excellence.
Provide executive technical leadership to a unified research and engineering organization, defining the technical architecture, strategy, and engineering roadmap for the Voice AI Platform.
Drive execution, ensuring the seamless translation of product requirements into robust technical specifications and successful delivery of next-generation voice intelligence solutions that are highly scalable and align with overall product and business strategy.
Manage and scale a unified Voice AI research and engineering organization distributed across multiple geographies, overseeing multiple teams, and defining the strategy for recruiting, mentoring, and attracting world-class talent.
Own the entire engineering lifecycle for central, reusable Voice AI models and foundational AI infrastructure, including establishing best-in-class MLOps practices for scalable training, efficient deployment, continuous monitoring, and performance optimization across edge and cloud environments.
Define, implement, and track key technical performance metrics (e.g., latency, throughput, model efficiency, system reliability) to measure engineering success, identify bottlenecks, and drive continuous improvement in execution and delivery.
Evaluate and integrate cutting-edge Voice AI research and technologies.
Proactively identify and mitigate significant technical risks, and lead critical engineering decisions, including build-versus-buy analysis.