Lead Product Manager

About the role

Dialpad is seeking a product leader to own the lifecycle of our core AI models, including custom SLMs, ASR stacks, and real-time inference infrastructure. This position focuses on the technical layer of our platform, requiring deep involvement in model behavior, evaluation, and training data rather than standard administrative product management.

Key facts

What you'll do

• Manage the full model lifecycle from data acquisition and training to production monitoring and retirement.
• Define data strategy, covering usage rights, consent, sampling, and annotation quality.
• Translate ambiguous quality issues into actionable metrics and clear release decisions.
• Participate in error analysis, incident retrospectives, and evaluation reviews as a technical peer.
• Support internal product teams by providing model capabilities and defining latency and cost constraints.
• Make trade-offs between model quality, streaming latency, infrastructure costs, and training methods.
• Produce written decision documents, direction memos, and technical specifications.
• Set the roadmap for model development, including deprecation schedules and labeling priorities.

Requirements

• Hands-on experience building, training, fine-tuning, or optimizing models in a production environment.
• 2+ years of product ownership experience where you held accountability for system outcomes.
• Technical fluency in evaluation design, quantization, serving trade-offs, and inference cost drivers.
• Ability to make decisions and commit to outcomes despite the probabilistic nature of model behavior.
• Direct communication style with a preference for written documentation over slide decks.

Nice to have

• Specific background in speech technology, including ASR, TTS, telephony, or streaming latency.
• Experience managing training data pipelines, labeling operations, or data rights.
• History of managing inference infrastructure at scale, including GPU capacity planning or cost-per-call optimization.
• Practical experience building or operating production evaluation harnesses.
• Experience with the pricing or packaging of AI-driven products.

Skills & tools

• Custom SLMs
• ASR stacks
• Real-time inference infrastructure
• Evaluation frameworks
• Data annotation and pipeline management