Senior Product Engineer (Full Stack, Frontend focused)

Building the Future of AI Teammates

Hello there. We're Voiceops, and we're ushering in the era of AI Teammates—where AI doesn't just analyze calls, but works alongside every member of a company’s go-to-market organization to turn the "aha!" moments from customer conversations into better execution across the company.

The Opportunity

The opportunity is massive. Every company selling to consumers has hundreds of thousands or millions of conversations with their customers each month. These conversations contain the blueprint for the company's growth, yet most organizations only see the tip of the iceberg of what's happening in these interactions. It’s petabytes of unstructured audio data that contain hidden patterns, and that can transform entire businesses. And these aren't small markets—we’re talking insurance, travel, financial services, education—industries that form the backbone of the economy.

Now the stars have aligned. The latest AI capabilities multiply the impact of everything we've built. What once required armies of people happens automatically. Sales reps get served the very best strategies for overcoming tough objections from the AI's observations of all calls with that objection, marketing sees emerging trends before the competition, and product teams discover new feature needs directly from customer calls.

What Makes Us Different

While others rushed to market with AI that looks good in demos but falls apart in production, we focused on the engineering foundation. We built our own model orchestration platform and AI builder called "Wavelength" — a system that captures and amplifies our customers' own expertise in AI models, and does so quickly. We ran over 500 experiments to ensure our models truly learn to think like their best people. It works.

We're pioneering generative AI interfaces that feel smooth, real-time, and reliable. We process audio streams, build complex data pipelines handling millions of conversations, and craft React interfaces that distill millions of conversations into simple, intuitive insights and actions. When our system identifies a compliance risk in real-time or surfaces a selling strategy that's 3x more effective, our objective is for this information to get to the right people at the right time.

Our customers see the usefulness behind what we've built—they're pulling us into strategic conversations because we're delivering what we promise, and what we promise is like magic to companies that have been operating at 10% visibility for ages. It's like upgrading from a grainy old tube TV to 4K HD. They can finally understand their customers clearly. 

The Interesting Problems

AI is moving at breakneck speed, which makes our work fascinating and creates a lot of open and interesting questions: How do you help companies quickly and reliably train AI models that capture their unique expertise, and have a feedback loop mechanism so that models continuously learn? How do you build trust so they feel they can rely on the AI's outputs? These problems require solutions that span data-labeling, transferring models across companies, inventing new UI patterns for generative AI reports, and more.

Moving at AI's pace means exactly that - moving fast. When OpenAI released their reasoning model (o1), we built and shipped a new feature with it in just 2 days. That's our speed. We're constantly integrating new models into our platform while expanding our own ideas about what’s possible to deliver to customers as foundation models break new ground.

We're designing fresh interfaces for AI-human collaboration. AI should feel like working with your best colleague, but how? This shift is happening across different industries and job functions. In enterprise settings, the interface probably won't be ChatGPT-style chatbots for most use-cases. How humans and AI work together is something we get to figure out, which creates really interesting front-end challenges.

Our "synthesis" interface is a perfect example - it sits between traditional dashboards and qualitative analysis. Think Perplexity-style interface, but hardened for enterprise use and specifically built to extract and communicate insights from customer calls. We get to help design how humans and AI interact in the workplace.

Our Engineering DNA

The team is a huge reason for our success to date. We've been working together for years and have hit this perfect rhythm of technical excellence and speed. We're scrappy, drama-free, and operate in hours and days, not weeks and months. One VP of Sales said we "move with the alacrity of a puma" (did you know pumas move with alacrity?). Our investor described our team as having "midwestern values with an east coast work ethic."

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