Lead Product Manager

Dialpad•San Ramon, CA

51d•Onsite

About The Position

Dialpad is seeking a Lead Product Manager to join its AI organization. This role will focus on owning the product direction for the models behind Dialpad's AI platform, including custom SLMs, ASR stack, and real-time inference infrastructure. The position requires a deep understanding of AI model lifecycles, from data acquisition and training to evaluation, production monitoring, and retirement. The ideal candidate will have a strong technical background, ideally with experience as an AI researcher, applied scientist, or ML/AI engineer, and a proven ability to translate complex technical concepts into product strategy. This role is research-driven, evaluation-heavy, and requires fluency in understanding model behavior, serving infrastructure, and prompt engineering. The Lead Product Manager will be responsible for defining quality bars, making critical trade-off decisions, and communicating product direction through clear documentation. They will work closely with engineering and research teams to ensure the AI platform meets the needs of internal teams and external customers, while also considering latency, cost, and scalability.

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, or with speech or with models under real-time constraints.
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.