We’re building an e-commerce marketing solution focused on visual content analysis and generation and deep customer understanding. If you have a startup mentality and a desire to help some of the world’s fastest growing brands leverage the latest ML/AI technologies, we want to hear from you. Genus AI is an international company with offices in US 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 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 degrees 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.

• Most of our codebase is written in Python (3.11+). We understand that languages can be learnt, therefore we are more interested in your engineering ability
• 2+ years of professional experience
• Have experience with one or more of the following: web development, relational databases, data engineering, machine learning and/or cloud infrastructure
• Excellent communication skills and ability to collaborate with diverse teams
• Enjoy building software by making small and iterative changes
• Have a good understanding of security best practices and basic cybersecurity hygiene
• Enjoy sharing knowledge, teaching and mentoring young peers

• €4,500 - €6,500 EUR gross/month
• The opportunity to join a generous stock options scheme. It is important to us that all of the employees could own a part of the company and share in the company’s success
• 25 work days of holidays
• A personal budget of 1000 EUR per year for learning courses of your choice, conferences, books, etc.
• All the tech you need to do your job.
• Hybrid workplace - office is always available, but you decide where to work from

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 a 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 automate things when it makes the most sense.

Languages, Frameworks and Tools you will encounter at Genus AI:

• Most of our codebase is written in Python (3.11+).
• 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 Github for most of our engineering (CI/CD) and product workflows.
• We use Django 4 with MariaDB for our client facing platform API.
• We use Celery, Docker, Kafka, AWS ECS, AWS Athena, AWS S3 for background tasks, ETL, and data engineering pipelines.
• 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. We leverage various GenAI tools such as CLIP embeddings, StableDiffusion and various LLMs. As we plan on doing more R&D there is an opportunity for validating any other cutting edge ML tools and frameworks.