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."
We talk to customers weekly, so we know we're building things worth building. We don't always nail features on the first try, but in truth our main bottleneck is simply bandwidth – there are too many valuable features our customers want, and not enough hours in the day. When we deliver these features, customers spend more money with us and we close new deals faster. That's why investing in product and engineering is a top priority right now. The best sales person on our team isn’t even a person - it’s the product itself.
What You'll Actually Build
As a Frontend-Focused Full Stack Engineer, you'll:
- Build novel human-AI interfaces, including our "synthesis" interface that transforms call data into actionable insights
- Create responsive React frontends with visualizations that make AI insights intuitive across millions of conversations
- Develop and scale our model orchestration platform and customer-facing web application
- Implement features that adapt to the evolving AI landscape while maintaining reliability
- Ship UIs that drive business decisions and help maintain our speed and quality as we grow
Key Qualifications
Technical Skills
- Frontend expertise with JavaScript/TypeScript in NodeJS and React ecosystem, Sass
- Experience with mature TypeScript environments (monorepos via npm, webpack, babel, ESLint)
- AWS experience, particularly with:
- Lambda functions, Aurora RDS, SQS, SNS, SES, S3, Step Functions
- CloudWatch, X-Ray for performance monitoring
- Testing tools like Selenium and Jest
Engineering Startup Mindset
- Previous early-stage startup experience
- Someone who finds it "unbearable not to do great work"
- Values simplicity and elegance in both code and system design
- Comfortable with technical ambiguity and taking architectural ownership
- Ability to move very quickly ("hours and days, not weeks and months")
- Prefers leveraging existing tools over reinventing wheels, but knows when to build custom
- Adaptable to new technologies and excited to explore cutting-edge AI capabilities (including using AI tooling like Cursor or Windsurf to work and build faster)
- Makes research-backed decisions while maintaining velocity
This Isn't For You If:
- You need detailed technical or design specs before writing a single line of code
- You prefer stability and predictability over constant iteration
- You prefer to write all code from scratch rather than leveraging existing tools
- You need extensive documentation and established processes before getting started
- You want siloed technical responsibilities with narrow scope
- You're uncomfortable with figuring out complex technical challenges as you go
- You're not ready to work hard in an early-stage startup environment
How We Engineer:
- Direct collaboration with the CEO and VP of Engineering on technical and product direction
- Fast iteration on features, architectures, and approaches—deploy daily
- Focus on what works now, not what might scale five years from now
- Team-first mentality—jump in to help debug and solve problems across the stack
The Details:
- Competitive compensation with meaningful equity in a high-growth company
- Fantastic benefits (including 401k matching, 100% of health benefits covered by us), flexible PTO
- Ground-floor opportunity to shape our technical architecture and engineering culture
Ready to Reinvent Human-AI Interfaces?
We believe this is the future of go-to-market: AI teammates that turn every call into deeper organizational intelligence and supercharge every team's performance. We hope you'll join us in building the systems that make it possible.
If this resonates:
- Send your GitHub profile
- Tell us why this role interests you
- Share your most impressive technical accomplishment and what made it successful
We'll respond within 48 hours. No form letters, no ghosting, just engineers talking to engineers about building something incredible.
Remote restrictions
- Must be a resident of United States
