Graduate ML Engineer
We’re looking for a driven and talented recent college graduate to join our R&D team. Someone who is very eager to improve and learn, and wants to use their skills to solve meaningful problems and build world-changing solutions.
As part of our R&D team, you will be implementing and testing state-of-the-art Machine Learning models, integrating them in our pipelines, and developing tools to automate time-consuming tasks, while learning about the best practices for Machine Learning engineering and software development.
Who we are
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.
We have productised our services into a web application and are continuously improving it to ensure that our best analyses and visualisations help our users get the maximum energy out of their assets. We operate at a large scale - millions 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. This is not your average dashboard, we’re talking about intelligently visualising and handling large quantities of data to drive performant visualisations and functionality.
Why do we need you?
The R&D team at Jungle has several areas of responsibility:
- Coming up with new clever (Machine Learning) problem formulations based on desired product improvements.
- Conceptualising, implementing and testing new state-of-the-art Machine Learning and deep learning models.
- Integrating new models in our Machine Learning pipelines.
- Developing tools, metrics, and algorithms to automate the more time-consuming tasks that occur when rolling out products to customers.
- Dissemination of technical knowledge - explaining our research to the other teams, particularly to the Service Delivery team, which uses our ML models to create insights for our customers.
We need people to help us in these areas. There’s enough flexibility that you can choose which area(s) appeal(s) the most to you at the start, e.g. focussing on Machine Learning research vs task automation. Over time you will be able to explore new areas if they are aligned with your goals and interests.
Why work with us?
- Join a funded scale-up.
- Learn and work with modern technologies (both in ML and software engineering).
- You have the opportunity to use your skills to create a meaningful change in this world.
- Become part of a warm and skilled group of people, committed to each other's success.
- We care about your growth and assign you a personal mentor to help you achieve this.
- As a remote-first company, we offer you a fully flexible work schedule, holiday policy, and work location.
- You have or are about to finish a MSc degree in a STEM area, for example, Computer Science, Data Science or related field. Exceptional BSc candidates will also be considered.
- You have some 'hands-on' experience with Machine Learning and/or software development - for example, a previous short internship, some personal projects (e.g. GitHub, Kaggle), a relevant MSc thesis, etc.
- You are comfortable with Python and the Python ML stack (e.g. Pandas, Sklearn, PyTorch, Numpy).
- You are familiar with Deep Learning techniques (e.g. neural networks, recurrent models).
- You are familiar with Transformer architectures in Deep Learning.
- You have a basic knowledge of git.
- You can join us for 6 months, and ideally have the availability and desire to transition into a full-fledged ML engineer role at Jungle after the program.
- You are very eager to learn and improve.
- You have great analytical and problem solving skills.
- You are very curious and won’t stop searching until you find the answer.
- You work meticulously. People around you trust your work results, and rightly so.
- You’re pragmatic; you know when to trade off diving deep with quick fixes.
- You are eligible to work in the European Union.