Lead Product Manager

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.

Scrappy

Curious

Optimistic

Persistent

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.

Own the data strategy underneath it all: acquisition, consent and usage rights, sampling, and annotation.

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.

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.

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 or with models under real-time constraints counts double. A CS/ML degree alone doesn't count; neither does "worked closely with data scientists."

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. Opinions you can defend to someone who does this full-time.

Judgment under uncertainty. Model behavior is probabilistic; roadmaps aren't. 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 have

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 we can read before we talk.

California Salary Range
For exceptional talent based in California, the target base salary range for this position is posted below. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the target range for new hire salaries for the position. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.

California Salary Range: $210,500—$266,000 USD

Why Join Dialpad

Work at the center of the AI transformation in business communications.

Build and ship agentic AI products that are redefining how companies operate.

Join a team where AI amplifies every employee's impact.

Competitive salary, comprehensive benefits, and real opportunities for growth.

We believe in investing in our people. Dialpad offers competitive benefits and perks, cutting-edge AI tools, and a robust training program that help you reach your full potential.

We have designed our offices to be inclusive, offering a vibrant environment to cultivate collaboration and connection.

Our exceptional culture, repeatedly recognized as a Great Place to Work, ensures that every employee feels valued and empowered to contribute to our collective success.

Dialpad is an equal-opportunity employer. We are dedicated to creating a community of inclusion and an environment free from discrimination or harassment.

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