Lead Product Manager

Lead Product Manager

Product Management, San Ramon, US

Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage.

Our AI organization builds, runs, and hosts the models behind our products: custom SLMs, our ASR stack, and the inference infrastructure that serves them in real time. Products like our voice agents and agentic runtime are joint efforts with product engineering — but the models they run on are built here, and this role owns product for exactly that layer. It's a different job from the rest of platform and product engineering: research‑driven, eval‑heavy, and closer to training data and model behavior than to sprint boards.

The standard PM toolkit doesn't cover it. The day‑to‑day runs on eval reports, latency budgets, and knowing whether a failure is a model problem, a serving problem, or a prompt problem — and without that fluency, even an excellent PM ends up coordinating from the outside instead of deciding from the inside. This is not a role you can do at the API‑orchestration level.

You need to know how models are built and run, ideally because you've built them.

We're hiring someone who won't have that problem. You've been on the other side of the table — as an AI researcher, applied scientist, or ML/AI engineer — and you've since moved into product, or you're ready to. You don't need a translator between you and the people building the system, and they don't need one between them and you.

Scrappy, Curious, Optimistic, Persistent, and Empathetic are core traits we seek.

• 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. Retirement is a real part of that: the leading labs deliberately sunset models to concentrate effort, and we'd rather run a few models well than maintain a legacy model zoo.
  
  That's the whole job — not one rotation among many.
• Own the data strategy underneath it all: acquisition, consent and usage rights, sampling, and annotation. Model quality is decided here before the first training run — get the data model right and every ASR and SLM effort downstream gets simpler and better. On a platform built on customer conversations, consent and rights are foundational, not paperwork.
• Turn ambiguous model‑quality questions into decisions. “Transcripts got worse this week” is a starting point, not a ticket. You'll define what good means, get it measured, and decide what ships.
• Sit inside eval reviews, error analyses, and incident retros as a peer. You should be able to look at a failing conversation trace and form your own hypothesis before the team tells you theirs.
• Treat internal teams as customers. The voice agents, agentic runtime, and AI features across the product all run on your stack — product engineering needs model capabilities and latency/cost envelopes they can plan around, and GTM needs a roadmap you won't have to walk back.
• 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. These are the daily currency of this role, not edge cases.
• Write. Direction memos, decision docs, and specs that engineers actually read. If your best work happens in slide decks, this isn't the right fit.
• Release and rollback calls: whether a model ships, against quality bars you define.
• The model roadmap and its sequencing — including what gets deprecated and when.
• Where data investment goes: acquisition, annotation, and labeling priorities.
• The quality bar itself: what “good enough” means for an ASR or SLM release, and how it's measured.

• A hands‑on track record with models themselves. You've built or run models in…