- Demonstrable experience (3 years) in developing and maintaining backend technologies for modern web applications in production and internal tooling; ideally experience in Python.
- Experience with SQL (eg. Postgres) and/or NoSQL (eg. Redis) databases.
- Experience with container/orchestration tools (Kubernetes, Docker, etc.)
- You have production-level experience in deployed databases running on scalable infrastructure.
- Experience in agile environments and development workflows using git or similar tools, and CI/CD tools such as Gitlab CI or Jenkins.
Preferred Requirements (extra)
- Experience with one or more of FastAPI, Prometheus, Grafana would be a plus.
- Experience with workflow orchestration tools (Airflow, Argo, Tekton, etc).
- (Bonus) You understand what is a Machine Learning model and have experience with MLOps.
- (Bonus) Some experience with front-end frameworks like Vue.js can be useful to contribute with functionality to our internal MLOps platform front-end.
- You are curious and won't stop searching until you find the answer.
- You work meticulously. People around you trust your work results, rightly so.
- You're pragmatic; you know when to trade off diving deep with quick fixes.
Running a flexible Machine Learning engine at scale is hard. We must ingest and process large volumes of data uninterruptedly and store it in a scalable manner. The data needs to be prepared and served to hundreds of models constantly. All the predictions of the models, as well as other data pipelines, must be stored and reachable for our web application(s) to present the generated insights to our customers.
We work on the system that delivers this functionality and also allows the Machine Learning engineers to deliver new and improved models at ease, manage existing models, monitor these models, and many different interactions, all of which are crucial to day to day operations.
You will be working and interacting with a wide array of technologies that constitute Jungle's core systems (data handling/processing, serving ML models, etc...) and building the backend systems that provide access to all this functionality. You will have the possibility to work on and enhance the different stages of an end-to-end Machine Learning system at scale.
Why do we need you?
- You’ll make use of modern open-source technologies in a practical use case to improve usability, performance and robustness of our internal system.
- You’ll work together with the engineering team to maintain and improve existing systems, and overcome difficulties arising from scaling up our systems to more and more data. Some examples:
- Contribute to the improvement of the new data backend that is used to efficiently serve large amounts of data to our product.
- Improve our workflow orchestration and making it more reactive to live data ingestion events.
- Improve observability of our systems to make sure that data flowing through our systems is in perfect conditions or otherwise notifying the team as early as possible.
- You’ll make architectural decisions on how to solve our engineering challenges and keep us future proof.
- You’ll research new (upcoming) technologies that will considerably improve the user experience and or development time of our products.
Jungle develops and applies Artificial Intelligence to increase the uptime and performance of renewable energy sources. Built on existing sensors and data streams, the company’s technology enables solar and wind energy owners to squeeze more out of their assets, accelerating the world’s transition to renewable energy sources.
Why: Operational complexity - such as the one of wind turbine performance - has reached a level that's beyond what our minds can grasp. Our tools enable the world to conquer this operational complexity and give back power to the people who manage it.
How: Through solidified Artificial Intelligence and Machine Learning expertise, the company’s technology leverages massive streams of data to understand the normal behaviour of an electro-mechanical asset, picking up on opportunities for better performance and risks of downtime, and continuously informing its users on what to prioritise.
What: Based in Lisbon, the Dutch-Portuguese deeptech company has productised its services into a web application - Canopy - and is continuously improving it to ensure that the best analyses and visualisations help its users get the maximum energy out of their assets. Jungle operates at large scale - billions of data points per day - providing always-on predictive models, alarms and metrics visualisations for some of the largest and most sophisticated customers in the global renewable energy space.
We hire remotely and globally (for candidates who are willing to work in the GMT+1/GMT+2 time zone)!