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.
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 guesswork from the scientific process, as 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.
1 Open Positions
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.
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.
11 Open Positions
We are fans of open source projects and believe in not reinventing the wheel. We use a new PHP7 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 Vue.js to power our front-end application in addition to other technologies like Redis, MongoDB, MySQL, Jenkins, Express and node.js.
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!
As a team, we’re very open to adopting new technologies. In fact, we’re constantly looking for the best tools for the job and if we can’t find it, we’ll develop our own (we developed our own ink engine and also built the foundation for collaboration). While we are very deliberate about the technologies we use, we don’t require engineers to have experience with any of them beforehand. We believe that anyone can learn a new language on the job. If anything, one of the most important attributes we look for in candidates is having a growth mindset.
When we started, Lightning wasn’t even in Beta, so we’re right on the bleeding edge. This choice in technology has come with its fair share of challenges (i.e. we’ve had to implement key features ~6 months before they get upstreamed), but it has also allowed us to build significant new features that were never before possible, and these key features are our differentiators. Our ability to trade cross-chain, giving access to real Bitcoin, and our high performance without sacrificing custody is our core product and value and both are the result on building on this brand new platform.
We predict that in 5 years, there will be far more engineers working on Bitcoin, cryptocurrency, and Lightning than there are today, and we want Sparkswap to contribute to that growing trend. A core competency of the company is to bring in people who are interested and enthusiastic about our technology and teach them how to work with it. In other words, you are not required to have experience with Lightning to apply or join. (If we had this criteria, we’d be limited to about 200 engineers worldwide :)
1 Open Positions
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.
Modernizing how B2B companies manage invoice-to-cash
Lawrenceville, NJ / Denver, CO / Woodbridge, NJ
We hold regular architecture review meetings where all team members can present how they are proposing to solve a problem, including what new technologies might be used. At Billtrust, a combination of architectural standards and team-based decisions drive the adoption of new technologies. For example, we had three scrum teams accustomed to building monolithic traditional three-tier manually tested software transition to containerized microservices with full CI/CD using a 70% different tech stack, on AWS, in one year.
Our microservices architectures are inherently platform vs. vertical application-oriented which allows for massive reuse and rapid time to market execution. We believe in full CI/CD pipeline implementation and we rabidly pursue automating everything. Our next-gen Quantum platform is distributed, event-driven and leverages a microservices architecture — it’s written from the ground up as cloud-native, which enables us to build scalable, reliable systems and quickly iterate.
Full-stack experience is very helpful as well as some experience with cloud-based platforms. That said, no engineer is going to know everything and we are open to hiring engineers who are willing to learn on the job. If you are language agnostic, know how to deliver incrementally without having to 'gold plate' software, see the value in CI/CD, and love to learn, we’d love to meet you. We provide a fair amount of opportunity for learning on the job, pairing with other engineers who have different skills, and regular lunch-and-learns.
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.
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