Hacker Python / Machine Learning

About the Role

We’re looking for a well-rounded, nimble engineer to support our 4 person ML team. While we take care of building the ML models and making sense of the data, your job will be to help us get those models into production. This will include turning our code into full-fledged services, rolling the infrastructure and CI/CD pipelines, integrating with other systems, as well as helping us with our data collection and ETL needs.

What will you do? 

• Integrate our ML models into various systems across the company
• Cooperate with our Data Engineering team (USA) to design and implement data pipelines and ETLs we need to (re)train and maintain our ML models
• Work with our DevOps team (USA/Prague) on designing and setting up our new MLOps infrastructure
• Set up monitoring and dashboards so we can keep an eye on our models’ performance
• If interested, help us drive the product side of things by taking part in finding and analyzing gaps and opportunities in automation, data collection, and/or on the ops side of things

What do we expect from you?

• Previous practical experience
• Ability to quickly navigate new systems and pick up the basics of any tech stack
• Being able to strike a good balance between good quality code and delivery time
• Willingness to be a pro-active member of the team
• A responsible attitude towards your projects and your team
• Willingness to learn and grow
• Fluent level of English

What about our team?

• We like our work and try to make going to work a good experience
• We try to stay in touch with the relevant new technologies and ideas
• We hope to learn from you and will help you grow and succeed where we can
• We move fast and have a significant say in determining what we deliver, as long as we drive value

What tools do we use?

• We use Python, Ruby on Rails, and JavaScript/TypeScript, but we won’t discriminate if you’re coming from a different background
• For ML & Data, we mostly go with Jupyter notebooks, Numpy & Pandas, DVC, Keras, and Airflow
• We use GitHub, do proper code reviews, utilize Circle CI/CD pipelines to run our tests and deploy to Kubernetes or AWS Lambdas
• We run our infrastructure in the AWS cloud and monitor applications in New Relic
• We use Shape Up as our delivery methodology

What we offer as a company?

• Flexible working hours (really)
• We support home office or remote
• Multisport card
• Fully paid certification on AWS
• Education allowance $2000 per year
• Company equity package
• A beautiful office in Karlín with a full fridge, amazing coffee and an Xbox
• Every Tuesday company breakfast, and occasional lunches and dinners
• Regular team buildings (game nights), corporate, and sports events (bouldering, volleyball, ice hockey, swimming, running)
• Occasional trips to the U.S.