We're building an Enterprise AI platform focusing on deep customer understanding, modelling and emotionally intelligent customer engagement. If you have a startup mentality and a desire to help some of the world’s largest organizations leverage the latest ML / AI technologies we want to hear from you.
Genus AI is an international company with offices in San Francisco, London and Vilnius. Come join us to change the world in a meaningful way!
The engineering team at Genus AI works closely with the commercial and data science teams to build a high-quality Enterprise AI platform and services that exceed client expectations. Our clients trust us with their most sensitive information and we make security a first-class consideration not only in engineering matters but across the whole company.
We are looking for front-end engineers with varying degrees of experience and from diverse backgrounds to help us build world's first customer modelling platform for emotional intelligence.
- Build and maintain features and user interfaces of Genus AI platform.
- Collaborate with product managers, designers and back-end engineers to deliver client-facing features.
- Debug production issues across multiple levels of the stack.
- Continuously track and improve engineering standards, tooling, and processes.
You may be fit for this role if you…
- Have 2+ years of HTML/CSS experience, including concepts like layout, specificity, cross browser compatibility, and accessibility.
- Have 1+ years experience with browser APIs and optimizing front-end performance.
- Have a good understanding of security best practices and basic cybersecurity hygiene.
- Enjoy building software by making small and iterative changes.
- Enjoy sharing knowledge, teaching and mentoring your peers.
Bonus points if you…
- Dabbled with serverless and GraphQL in personal or professional projects.
- Dabbled with Python and Django in personal or professional projects.
- Dabbled with managing AWS infrastructure in personal or professional projects.
- 2000-4000 EUR net monthly salary or more.
- A personal budget of 1000 EUR per year for learning courses of your choice, conferences, books, etc.
- 25 work days of holidays.
- Competitive salary.
- All the tech you need to do your job.
- Unique opportunities to grow professionally and as part of the team.
What is engineering at Genus AI like
First and foremost we want to be proud of our team, the work we do together, to learn from one another and set an example and be driving force in AI application in positive ways.
We follow Gall's Law
when we build systems - start simple and gradually improve. We build systems that are not algorithmically pure, but easy to change, adapt and improve
. We are fans of "Choose Boring First
" approach and try to be smart about the technologies
We are optimizing our engineering towards sustainable speed
. We encourage the release of small iterative changes, that are fast to review and verify. This gives us a significant speed in trying out various ideas and getting feedback fast.
We review everything that goes into production, both to improve the quality of our code and to share knowledge. We use a subtly different review workflow
that contributes a lot
to our productivity and speed.
We automate things
when it makes the most sense
In all of the above, we strive to be keep a high-security standard. We use a set of tools to automate compliance checks. We do regular software upgrades, review our software dependencies and regularly organise pen-testing. We enforce good basic cybersecurity hygiene wherever possible and expect every team member to support this. Languages, Frameworks and Tools you will encounter at Genus AI
Most of our back-end is written in Python (3.6+). We understand that languages can be learnt, therefore we are more interested in your engineering ability.
Most of our front-end is written in TypeScript (3.7+), React and Apollo GraphQL.
Our infrastructure is built on top of AWS and we use CloudFormation for Infrastructure-as-a-Code, and Ansible with custom plugins for configuration automation.
We use Phabricator
for most of our engineering and product workflows. It has an excellent code review system, among many other tools.
We use Django 2 with MariaDB as our platform back-end. With some PySpark and PrestoDB here and there.
We use Jupyterlab with Python, Scikit-learn, Tensorflow and H2O.ai for data science workloads. As we plan on doing more R&D there is an opportunity for validating any other cutting edge ML tools and frameworks.