Lead Product Manager

Dialpad•San Ramon, CA

52d•$210,500 - $266,000•Remote

About The Position

Dialpad is the AI-native business communications platform that unifies calling, messaging, meetings, and contact center, powered by AI that understands every conversation in real time. Over 70,000 companies globally rely on Dialpad. The company is leading the shift to Agentic AI, with its DAART initiative (Dialpad Agentic AI in Real Time) redefining what a communications platform can do. Dialpad believes AI amplifies every employee's impact, providing powerful AI tools to help teams move faster, think bigger, and achieve more. They seek individuals who are intensely curious, hold themselves to a high bar, and embody core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic.

Requirements

A hands-on track record with models themselves. You've built or run models in production — trained, fine-tuned, served, or optimized them — not just orchestrated APIs around them.
Experience with custom SLMs, ASR, and the inference infrastructure behind them.
Experience with speech or with models under real-time constraints counts double.
2+ years of product ownership, formally titled or not. You've been accountable for what got built and whether it worked, not just for the backlog.
Fluency across the model and serving stack. You have informed opinions on eval design, when to fine-tune vs. train vs. distill, quantization and serving trade-offs, why WER alone is a lousy ASR metric, and what actually drives real-time inference cost.
Judgment under uncertainty. You can commit to outcomes without pretending the uncertainty away.
Direct communication. You say what you think, change your mind when the evidence says so, and put decisions in writing.

Nice To Haves

Speech experience specifically: training or productionizing ASR/TTS, telephony, streaming latency work.
You've built training data pipelines or run labeling operations — sourcing, sampling, annotation quality, data rights.
You've run inference infrastructure at scale — GPU capacity planning, serving optimization, cost-per-call tuning.
You've built or run an eval harness in production, not just read about them.
Experience pricing or packaging AI products.
Publications, open-source work, or a technical blog.

Responsibilities

Own product direction across the full model lifecycle — data, training and adaptation, evaluation, release, production monitoring, and improvement or retirement — for our SLMs, ASR stack, and the real-time inference infrastructure that serves them.
Own the data strategy underneath it all: acquisition, consent and usage rights, sampling, and annotation.
Turn ambiguous model-quality questions into decisions.
Sit inside eval reviews, error analyses, and incident retros as a peer.
Treat internal teams as customers.
Make trade-off calls with real constraints: model quality vs. streaming latency, train vs. fine-tune vs. buy, model size vs. capability, GPU cost vs. what the price point can absorb.
Write direction memos, decision docs, and specs that engineers actually read.
Make release and rollback calls: whether a model ships, against quality bars you define.
Define the model roadmap and its sequencing — including what gets deprecated and when.
Determine where data investment goes: acquisition, annotation, and labeling priorities.
Define the quality bar itself: what "good enough" means for an ASR or SLM release, and how it's measured.