Lead Product Manager

About this role

About Dialpad 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.

Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved.

Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile.

Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more.

We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves.

We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic .

Your role 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. This role has the opportunity to be based in our Bay Area office.

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 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.

And what you don't own, so there's no bait-and-switch: modeling and architecture choices belong to the engineers and researchers making them, and research bets and headcount are set with you, not by you. If a model regresses in production, accountability lands here first — the authority above is what makes that fair.

Skills you'll bring

• 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. Building beats running. Our core work is custom SLMs, ASR, and the inference infrastructure behind them, so experience with speech

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