Our speech process system translates videos into the World’s languages without losing the characteristics that make each individual’s voice unique.

It’s a unique technology that has never existed before. This is only possible with a great engineering team composed of talent from Cambridge, Oxford, NYU, and UCL, and we have the world’s leading academics in speech processing - Simon King and Mark Gales as our advisors.

We’re looking for machine learning engineers who are comfortable with frameworks such as TensorFlow and PyTorch, or are willing to push themselves to know these frameworks like the back of their hand.

We’re also looking for full-stack (Python/JS) developers who are customer oriented and have the ability to keep iterating the client-facing self-service platform as we navigate unexplored territories. Our developers also work closely with ML Engineers to streamline the pipeline, and enforce the discipline needed for a tidy, clean, production-ready code-base.

We're a deep-tech company doing hard science and solving difficult problems on both the technical and science side.

Wherever possible, we avoid reinventing the wheel. If you've come hoping to build the next JS framework or cloud platform, you're in the wrong place. Our stack consists of tried and tested products. That said, if you feel like a particular tool would really help us solve a problem, we’re totally open to experimenting!

We're trying to work with huge datasets to create human-understandable solutions to very complex problems, and are pushing the boundaries of ML and AI as a result. The number of proteins in our search space is larger than the number of elementary particles in the known universe - searching that vast array of possibilities requires cutting-edge tech and a lot of out-of-the-box thinking!

We're also trying to remove human bias and guess-work from the scientific process. So much of the manual lab work we do relies on intuition and hunches built up through years of training and practice in the lab. We're looking to interrogate that knowledge, validate it, and embed it into automated systems so that every process can make the most of the years of experience of our team, as well as the rigour and repeatability of automation.

We are platform agnostic.

Software for new hardware is what we do best, and our engineering team is constantly experimenting with new technologies. Currently, our team is excited to work on VUI (voice) projects and expand our AI/ML practice. We are working very closely with industry AR/VR leaders and undertaking interesting and unique applications of these technologies.

A lot of what we do hasn’t been done before.

In fact, when Grammarly was founded in 2008, the concept of using AI to facilitate communication was a completely new concept. We managed to build something that was profitable right from the get-go because we were interested in solving problems at the next frontier, and we still continue to be today. We’re always ready to try out and adopt new technologies (check out our tech stack below for the variety of languages, tools, and frameworks we use both internally and in production). And we’ll often customize open-source tools for our own use, like building a custom layer on top of Docker. It’s also very common for our engineers to spontaneously give talks and tutorials on the things that interest them. We have a group of functional-programming evangelists, and one of the most recent talks was called “Fighting God Object with Monads.”

Check out our tech blog for the challenges we’re working on.

We are constantly adding new technologies to our stack to make our software development process faster and more efficient.

We are fans of open source projects and believe in not reinventing the wheel. We use a new PHP framework called Laravel to power our API layer. It gives PHP super-powers like command line tools, dependency injection, ORM, queues and schedules, event listeners, and so forth. We also use Ember.js to power our front-end application and we are soon refactoring to Ember Engines. This is in addition to other technologies like Redis, MongoDB, MySQL, Jenkins, Express and node.js.

Braze is at the forefront of using new technologies to meet customer needs.

We are currently building a real-time data processing infrastructure using Kafka. Our push sending infrastructure is instrumented through Go microservices. Our mobile SDK team includes support for the latest mobile frameworks, such as React Native.

We’re operating at a scale where we need to mix established software with state-of-the-art technologies to support our product (e.g. our API processes billions of data points per day from hundreds of millions of devices).

Ohhhhh shiny!

Our stack includes Ruby on Rails, NodeJS/Serverless, and DynamoDB, just to name a few. We aim to increase developer velocity by using new technologies with large community resources. We realize that building applications with dated technology discourages other people from wanting to work with us and lowers our educational resources online. Open source technology has allowed us to get answers to our questions faster, which in turn has helped us build faster. We ship code everyday and are constantly rolling out new features to our customers. We decide what needs to be built and then we look at which technology will get us there the fastest. You may not have experience with the Serverless Framework, but as long as you are open to learning it, we are open to teaching it to you!

Build with the latest and greatest.

Our stack includes GraphQL, React, React Native, TypeScript, and Ruby. We ship every day to our server as well as our web, mobile, and iPad apps. We’re committed to using new technologies to improve our development process and speed. A typical week looks like a quick sprint planning meeting to decide on which features we want to build, daily standups to measure our progress, and a high-degree of autonomy to build quickly.

We enjoy learning and utilizing the latest technologies to improve our product and process.

Our stack uses Node.js, React, Redux, Swift, and Postgres. On the backend, our API uses Koa, which is the modern successor to express that utilizes async/await. This has allowed us to make our software development faster and simplify the code while reducing tech debt. Our frontend utilizes React + Redux, and we actively encourage experimenting with new and upcoming frontend technologies.

We encourage experimentation and look forward to the new tools we can implement in the coming years.

We build pretty cool tech, not by choice but by necessity of the customer need.

From the ground up, we built on a vast, data lake model using modern, scalable data science tools: Apache Spark (scala), high performance and fault-tolerant Go servers, and AWS lambdas all managed with CloudFormation and deployed with continuous integration. Our frontend is built on Google Firebase, and heavily uses GCP cloud functions, and we are currently building frontend in React JS. It’s important to note that we don’t chase novelty. We chose technologies because they’re the best tools for our customers’ job.

The problems we are facing are challenging even for world class data science teams. There are a lot of subtleties and irregularities in raw data, and managing predictive models at scale with a fast-changing product and user base is challenging. We work continuously with data science advisors to find creative new ways to solve modeling problems.

On top of the usual problems of scale and data science, we at ClearBrain build tools one layer of abstraction up. Rather than parsing each data set one-off, we engineer features for common data schemas across many customers, and have built a data transformation description spec that allows us to iterate on schemas without changing a line of code. Rather than build each model for a specific use case, we take on the challenge of abstracting away and assessing how to generalize machine learning for every company.