San Francisco
11 - 25 Employees
< 10 Engineers
$10M - $25M Funding
Series A

VoiceOps uses AI to analyze sales calls at scale and uncover issues.

Our average customer makes tens of thousands of calls per week In a world without VoiceOps, they have literally no idea what their sales reps are doing on the phone. It's a total (and scary) black box.

By applying ML and a great UI to this problem, call center leadership has all the data they need about customer conversations at their fingertips, and can root out problems they previously had no idea existed.

The technical problem is interesting, and gets more interesting as we grow. In a nutshell, we are building a system for structuring audio data that has to meet 3 criteria: —> Extraordinarily accurate (human-level accurate) —> Low-cost —> Scalable

Call center recordings are one of the richest/largest untapped datasets in the world (literally, billions of calls stored in AWS buckets that no one is touching right now). We're going to be the best in the world at structuring that data and putting it to use to make businesses work better.

VoiceOps photo 1 VoiceOps photo 2 VoiceOps photo 3 VoiceOps photo 4
Active Roles
Why join us?
  • Impact — Because the team is small, you’ll help set the template for engineering quality and process. You'll also be encouraged to contribute to product decisions, and help shape the direction of the company.

  • Stellar Team and Culture We have very high standards for our engineering team, so you'll get to work with some of the smartest people you've ever worked with.

  • Growth — We've just raised our Series A and are looking for more smart people to continue growing quickly. You’ll be one of the first few employees and have opportunities to be a leader on a growing team.

Engineering at VoiceOps
Engineering team and processes

We currently have 3 engineers, all of whom are strong full-stack generalists. We start the week with a product planning meeting where everyone on the engineering team and our CEO Ethan discuss the priorities for the week. We optimize for tight execution on a limited set of priorities, which creates a collaborative environment (we're often all working together on the next big user-facing project together) and mutual accountability.

Technical Challenges

Human conversations are messy. Previous software attempts at structuring conversation data leave a lot to be desired.

We are taking the same approach to conversation analysis that Uber/Cruise/Waymo are taking for self-driving cars. Building troves of training data, and solve lots of edge cases piece by piece towards the goal of having an incredibly reliable system.

Our core technical challenge is how to take billions of audio recordings (and messy, unstructured human conversations) and make sense out of that data in a way that is: a) accurate b) cost efficient, and c) highly scalable.

(The corresponding product problem is how to take well-structured data and make it actionable for the end-user)

Projects you might work on
  • Comments - a project which lets our users have dialog while on VoiceOps (similar to Google Docs), allowing feedback to be focused on specific moments in calls. This is a typical product feature where the engineer(s) working on it would own the data model, API design, and frontend implementation.

  • Implementation of a robust, configurable, thread-safe state machine to move calls through various analysis steps, with visibility if any of the steps are slow or failing. Before this project, calls could go through the same step multiple times or be processed out of order due to stale object references or multiple threads executing at once.

  • An integration system which imports call recordings from dozens of customer systems, supporting the analysis of thousands of calls per day. Challenges include failure visibility, networking/VPN constraints within customer data centers, and careful handling of timezones / date cutoffs with international customers.

Tech stack
Ruby on Rails

Working at VoiceOps

Every problem is our problem

We do not look to external sources for why we didn't hit a deadline, meet an objective, etc. We hold ourselves responsible.

Intellectually honest and curious

We challenge each other's ideas daily - on product strategy and beyond. At lunch we are more likely to talk philosophy than TV (though we do some of that too)

Emotionally open and vulnerable

We talk about the ups and the downs of our lives outside of work and strive to have high authenticity interactions with each other.


We work hard to be an enduring company that paves the way for all sorts of applications which require structuring verbal conversational data at scale.

High quality bar, and always rising

We redo our work again and again until it's great. (can be frustrating in the moment but is ultimately extremely rewarding when we build things that are greater than we imagined they would be)

Generous Vacation
Pet Friendly
Health Insurance
Flexible Hours
Team Activities
401(k) Contribution
Free Food


Maternal/Paternal Leave

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