Software Engineer, Back-end

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 an experienced software engineer with varying degree of experience and diverse backgrounds to work together.

You will...

  • Design, build and maintain Genus AI platform, cloud infrastructure, and machine learning pipelines.
  • Debug production issues across multiple levels of the stack.
  • Work with your team to build new features on Genus AI platform.
  • Work with the data science team to automate machine learning pipelines.
  • Improve engineering standards, tooling, and processes.

You may be fit for this role if you...

  • Have a good command of written and spoken English.
  • Have a good understanding of security best practices and basic cybersecurity hygiene.
  • Enjoy sharing knowledge, teaching and mentoring young peers.
  • Enjoy contributing to open source projects.
  • Have experience with one or more of the following: web development, relational databases, machine learning and/or cloud infrastructure.
  • Enjoy building software by making small and iterative changes.


  • 2000-3000 EUR net monthly salary or more.
  • 25 work days of holidays.
  • A personal budget of 1,000EUR per year for learning courses of your choice, conferences, books, etc.
  • All the tech you need to do your job.
  • Unique opportunities to grow professionally and as part of the team.

What is it like in engineering at Genus AI

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 expect initiative and self-reliance in a lot of day to day tasks.

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 pick.

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 codebase is written in Python (3.6+). A basic understanding and ability to follow PHP and/or Java code would be a plus. We understand that languages can be learnt, therefore we are more interested in your engineering ability.

Our infrastructure is built on top of AWS and we use CloudFormation for Infrastructure-as-a-Code, and Ansible with custom plugins for automation.

We use Phabricator for most of our engineering and product workflows.

We use Django 2 with MariaDB for our client facing platform API.

We use React, TypeScript, and GraphQL for our platform UI.

We use Jupyterlab with Python, Scikit-learn, Pytorch, Tensorflow, and AWS SageMaker 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.

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