We work from a synchronous schedule and we feel this is instrumental in our success. The advances we are making take a high degree of collaboration and we want everyone to be on hand during the day. We all start by 9am and finish up by 6pm. And, we all eat together as a team in the cafeteria down below. Lunch is catered by local companies and we work with a variety of dietary needs.
We are big on vacation and everyone starts with 5 weeks of vacation. After two years that grows to 6 weeks. Vacation is mandatory and we require that everyone take time off to ensure they are doing the best work of their lives.
Cyberdyne We are big believers in scrum as long as it is self-organized and self-enforced. This isn't a top-down but rather bottom-up and we expect every engineer at our organization to contribute and make the process work. Scrum isn't just for product development it is designed to bring together product owners and sponsors into one process. We work very hard to make sure the process is working and that what our customers want is the main driver as we plan and score new features.
Our testing is not as far along as it should be and it is something we are putting more resources into for the next 18 months. We do bi-weekly code reviews as a group (this is part of our retrospective process). We use those to help more people understand more of the code and improve quality going forward.
At Cyberdyne algorithmic integrations are pervasive across the business. We have dozens of data products actively integrated systems. That requires serving layer that is robust, agile, flexible, and allows for self-service. Models produced on Flotilla are packaged for deployment in production using Khan, another framework we've developed internally. Khan provides our data scientists the ability to quickly productionize those models they've developed with open source frameworks in Python 3 (e.g. PyTorch, sklearn), by automatically packaging them as Docker containers and deploying to Amazon ECS. This provides our data scientist a one-click method of getting from their algorithms to production. We then integrate those deployments into a service mesh, which allows us to A/B test various implementations in our product.s