Senior/Principal Product Manager

Say hello to opportunities.

If you’re looking to be part of what’s next in communication, you’re in the right place.

At RingCentral, we believe the best customer experiences happen when humans and AI work together. Our agentic voice AI portfolio—AIR, AVA, and ACE—brings together automation, assistance, and insights across the entire conversation lifecycle. The result? More seamless, intelligent experiences for businesses everywhere.

With $2.5B+ in ARR and $250M invested in R&D annually, we’re building the future of AI-powered business communications.

RingCentral is moving from AI that assists people to AI that completes work. Our AI-first customer engagement portfolio — AIR, AIR Pro, RingCX, and the AI agent capabilities we are shipping across voice and more than twenty digital channels — is being adopted at scale, with RingCX growing roughly 70% year over year and more than 1,700 business customers. We are turning individual AI features into a coherent agentic platform that enterprises can build on, govern, measure, and trust.

That is what this role owns. You will define and ship the platform layer that lets an AI agent reason over a goal, call real business systems, respect enterprise guardrails, hand off to a human with full context, and be evaluated with the same rigor a contact center applies to its best people. You will do it at a company that already owns the voice channel, the enterprise relationships, and the distribution — which means your decisions reach production traffic quickly, and mistakes are expensive.

We are looking for someone who has done this where it is hardest: enterprise conversational and agentic AI. If you have built agent platforms, agent builders, agent-assist products, or CX automation at a company competing in this category, you will recognize most of this description.

What you will own

Four surfaces, one product story. You will not own all four equally on day one — you and your leadership will sequence them — but you are accountable for how they fit together.

1. The agentic platform and agent builder

• Agent orchestration: goal decomposition, multi-step reasoning, and where the line sits between deterministic workflow and model-driven autonomy for a regulated enterprise buyer.

• Tool use and actions: how agents call CRM, ticketing, order management, and homegrown systems safely — schema definition, auth, retries, idempotency, timeouts, and failure semantics.

• Knowledge grounding and retrieval: connecting agents to knowledge bases, policy documents, and structured data, with attention to freshness, permission-aware retrieval, and source citation.

• Guardrails and control: topic boundaries, escalation triggers, human-in-the-loop checkpoints, approval gates for consequential actions, and PII handling.

• Agent lifecycle: authoring, versioning, simulation, staged rollout, rollback, and deprecation — with a no-

• code path for CX administrators and a pro-code path for developers on the same underlying model.

• Multi-agent patterns: supervisor and specialist agents, routing between agents, and shared context across a customer journey.

2. Developer platform, APIs, and ecosystem

• Public APIs, SDKs, webhooks, and event streams that make the agentic platform extensible by customers, systems integrators, and independent software vendors.

• Interoperability with the agent tooling standards our customers are standardizing on, including model- context and tool-interop protocols, and a clear position on where we adopt versus differentiate.

• Integration and connector strategy: what we build, what partners build, and what the marketplace surface looks like.

• Platform quality attributes treated as product requirements rather than afterthoughts: multi-tenancy, rate limiting, versioning and deprecation policy, sandbox environments, and identity and access management for both human users and non-human agent identities.

• Developer experience: documentation, quickstarts, reference implementations, and time-to-first-working-agent as a tracked metric.

3. Evaluation, observability, and model operations

• The evaluation system: offline test sets, conversation replay and simulation, model-as-judge scoring with human calibration, regression suites that gate release, and golden datasets built from real traffic.

• Production observability: transcript and trace inspection, tool-call success rates, hallucination and policy-violation detection, latency budgets, and drift monitoring.

• Model lifecycle: model and prompt selection, A/B and shadow testing, upgrade paths when a foundation model changes underneath us, and the cost-quality-latency tradeoff expressed as an explicit product decision.

• Unit economics: cost per conversation and cost per resolution, plus the levers — routing to smaller models, caching, context trimming — that move them without degrading outcomes.

4. Customer engagement outcomes

• The metrics the buyer actually cares about: containment and self-service resolution rate, first-contact resolution, average handle time, transfer rate, CSAT, and revenue outcomes for sales and retention use cases.

• Voice-native quality: latency and turn-taking, interruption and barge-in handling, ASR and TTS quality across accents, and graceful degradation on poor audio.

• Agent assist and the human-AI seam: real-time guidance, summarization, after-call work, and QA and coaching workflows built on conversation data.

• Multilingual and omnichannel parity, and the pricing and packaging implications of consumption-based AI agents.

How you will work

• Write it down. You own strategy documents, PRDs, and decision records specific enough for engineering to build from and honest enough to survive a design review.

• Get in front of customers. You will run design partner programs, sit inside real deployments, listen to real call recordings, and bring back the difference between what customers say they want and what their operational data shows.

• Build with the builders. You will prototype agents yourself in our own tooling. You are expected to be hands-on enough to break your product before a customer does.

• Sequence honestly. You will make explicit calls about what we are not building, and defend them to Sales and to executives with evidence rather than opinion.

• Partner across the seams. Agentic AI touches security, legal, privacy, pricing, support, and enablement. You will bring those teams in early rather than clean up afterwards.

What you need to have

We weigh demonstrated judgment in this domain far more heavily than years served. If you have less experience but have shipped something genuinely hard here, apply.

• Seven or more years in product management with a track record of company-level impact for Principal, including at least two years owning conversational AI, agentic AI, or ML-powered products in production.

• You have shipped an LLM-based product to real users at scale. Not a pilot and not a demo — something with production traffic, an on-call rotation, and consequences when it was wrong.

• Technical fluency to be a real counterpart to engineering. You do not need to write production code, but you must reason confidently about prompting and context management, retrieval, tool and function calling, latency and token budgets, fine-tuning versus prompting tradeoffs, and how architectural choices constrain the product.

• Platform product experience. You have owned APIs, SDKs, or extensibility surfaces where your users were developers or technical administrators, and you understand the discipline of versioning and backward compatibility.

• Enterprise B2B SaaS depth. You have navigated security reviews, procurement, admin and permission models, audit requirements, and the reality that the buyer, the administrator, and the end user are three different people.

• Quantitative rigor. You define success metrics before you build, instrument them, and change your mind when the data disagrees with you. You are comfortable in SQL or an equivalent analytics environment.

• Excellent written communication. Your documents are the artifact that scales your thinking. We will askfor a writing sample.

• A bachelor's degree in a technical, quantitative, or design field, or equivalent practical experience.

What will set you apart

• Direct experience at a conversational AI, agentic AI, or CCaaS and CX automation company — an AI agent platform, an agent-assist product, or an enterprise agent builder — where AI agent quality was your personal accountability.

• Hands-on voice AI experience: real-time streaming, sub-second latency budgets, ASR and TTS selection, and the operational realities of telephony.

• You have built or owned an evaluation harness for non-deterministic systems, and can explain candidly what worked and what was theater.

• Forward-deployed or solutions-adjacent experience: you have personally stood up an AI agent inside a customer's environment and owned the outcome, not just the requirements.

• Conversation design background, or credible collaboration with conversation designers on dialogue, disambiguation, error recovery, and persona.

• Contact center domain knowledge: routing, workforce engagement management, quality assurance, and the metrics a VP of Customer Care is measured on.

• Familiarity with the AI governance landscape enterprises now ask about — SOC 2, HIPAA, GDPR, PCI DSS,the EU AI Act, and recognized AI risk frameworks — and the ability to turn those into product requirements rather than blockers.

• Experience with consumption-based or outcome-based pricing for AI products.

• Multilingual product experience, or having shipped for markets outside North America.

RingCentral’s work culture is the backbone of our success. And don’t just take our word for it: we are recognized as a Best Place to Work by BuiltIn, the Top Work Culture by Comparably and hold local BPTW awards in every major location. Bottom line: We are committed to hiring and retaining great people because we know you power our success.

About RingCentral

RingCentral is a global leader in agentic voice AI–powered business communications, delivering an integrated platform for business phone, SMS, contact center, workforce engagement management, video collaboration, and messaging. As the communications layer connecting businesses and customers, RingCentral is the front door of business communication and is in the advantageous position to apply AI at every phase of the conversation journey — before, during, and after each interaction. Our agentic AI portfolio includes autonomous voice-first AI agents that automate calls, assist in the moment, and analyze every interaction – enabling businesses to work smarter, respond faster, and connect more meaningfully with their customers. Visit ringcentral.com to learn more.

RingCentral is an equal opportunity employer that truly values diversity. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We are committed to providing reasonable accommodations for individuals with disabilities during our application and interview process. If you require such accommodations, please click on the following link to learn more about how we can assist you.

If you are hired in California, the compensation range for this position is between $174,000 and $212,200 for full-time employees, in addition to eligibility for variable pay, equity, and benefits. Benefits may include, but are not limited to, health and wellness, 401k, ESPP, vacation, parental leave, and more! The salary may vary depending on your location, skills, and experience.