Lead Product Manager

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.

About the job

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.

Skills

Slm, Asr, Inference Infrastructure, Model Training, Model Evaluation, Data Strategy, Fine-Tuning, Quantization, Real-Time Inference, Eval Design, Speech Recognition, Training Data Pipelines