# Lead Product Manager

**Company:** [Dialpad](https://hotfix.jobs/companies/dialpad)
**Location:** San Ramon, CA
**Role:** Product Management
**Salary:** $211k – $266k/yr
**Experience:** 5+ years
**Skills:** slm, asr, inference infrastructure, Model Training, Model Evaluation, data strategy, Fine-Tuning, quantization, real-time inference, eval design, speech recognition, training data pipelines
**Posted:** 2026-07-17

> Lead Product Manager for Dialpad's AI models, owning the full lifecycle of custom SLMs, ASR, and real-time inference infrastructure. Requires hands-on ML/AI engineering or research background plus product ownership experience to make technical trade-offs on data, training, evaluation, and production without needing a translator.

## Job Description

## What you'll do
- Own product direction across the full model lifecycle (data, training and adaptation, evaluation, release, production monitoring, and improvement or retirement) for custom SLMs, ASR stack, and real-time inference infrastructure.
- Own the data strategy: acquisition, consent and usage rights, sampling, and annotation.
- Turn ambiguous model-quality questions into measurable decisions and define what "good" means.
- Participate as a peer in eval reviews, error analyses, and incident retrospectives; form independent hypotheses from conversation traces.
- Treat internal teams (voice agents, agentic runtime, AI features, GTM) as customers, providing model capabilities, latency/cost envelopes, and a reliable roadmap.
- Make trade-off decisions on model quality vs. streaming latency, train vs. fine-tune vs. buy, model size vs. capability, and GPU cost vs. price point.
- Write direction memos, decision docs, and specs; make release/rollback calls; own the model roadmap (including deprecations); set data investment priorities; define and measure quality bars.

## Skills you'll bring
- Hands-on track record building or running models in production (trained, fine-tuned, served, or optimized); experience with speech/ASR or real-time constraints is highly valued.
- 2+ years of product ownership (formally titled or not), with accountability for what was built and whether it worked.
- Fluency across the model and serving stack: eval design, fine-tune vs. train vs. distill, quantization, serving trade-offs, ASR metrics beyond WER, and real-time inference cost drivers.
- Judgment under uncertainty and direct communication; ability to commit to outcomes and document decisions in writing.

## Nice to have
- Speech experience: training or productionizing ASR/TTS, telephony, streaming latency.
- Built training data pipelines or run labeling operations (sourcing, sampling, annotation quality, data rights).
- Run inference infrastructure at scale (GPU capacity planning, serving optimization, cost-per-call tuning).
- Built or run an eval harness in production.
- Experience pricing or packaging AI products.
- Publications, open-source contributions, or technical blog.

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