As a senior python backend software engineer, you will drive the development of our core user-facing application, help us define the product vision, and play a pivotal role in building and leading an A* team!
Our current tech stack is Python Flask backend, powered by machine learning algorithms that have been developed over the course of 4 years, served using docker and AWS. You will have the opportunity to take complete ownership of major aspects of our product. We will trust you to solve our most difficult challenges and empower you to constantly learn about better ways of serving our customers. Finally, you will have an outsized impact of defining not only the technical roadmap but the entire culture of the company. Your mission
What you need to succeed
- Drive product development of our application backend, both through incremental improvement and entirely novel features.
- Improve our existing technical foundations, and design our technical strategy & architecture.
- Help us build and nurture an outstanding engineering team.
- Work closely with our frontend team, and with our customers, to build the right product that lets our customers succeed.
- Be outstanding and set the tone for everyone in the company!
- 4+ years of professional experience building SaaS products, ideally with Python flask (+ docker is nice to have).
- Experience with highly interactive or data-intensive web applications.
- Experience with at least one major cloud provider (AWS, GCP, Azure)
- The ability to work in an unstructured, self-directed environment.
- A love for building, especially novel product experiences.
- Care and empathy for users. We fall in love with our users, not our products.
- Ability to communicate clearly and succinctly.
- Experience with Computer vision is a bonus.
Our tech stack consists of a ReactJS (including d3.js graph rendering) + Python Flask + MongoDB web app served across multiple Docker containers on AWS. Some of our current challenges include:
- processing efficiently and visualising intuitively vast amounts of data;
- supporting deployments across different infrastructures.
- engineering creative ways to integrate hooks that ingest data from different ML infrastructures and architectures.