Machine Learning Engineer (f/m/d)

Intro

Exnaton is a startup, spun out of ETH Zurich, which offers software for renewable energy to utility companies. Our software enables energy providers to set up energy communities and smart energy tariffs in their distribution areas. Users of our software can track their energy consumption and buy renewable energy from their neighbors, which makes investing in the energy transition simple and fun!

Prosumers, dynamic electricity tariffs, and energy sharing are driving the energy transition and becoming integral components of the decentralized energy world. Our software is a cloud application that processes smart meter data and computes smart real-time prices for locally produced energy which makes renewable energy financially more attractive. End-users receive an app that maps the sources of their energy, making their electricity consumption more understandable.

Our team is growing and we are looking for extraordinary and talented people to complement our team. We are a diverse mix of engineers and business developers, all driven by the shared goal of using technology to create a sustainable impact in our society. If you're enthusiastic about contributing to this exciting journey, we invite you to be a part of our innovative and motivated team.

The job can be performed remotely in EU/EFTA countries.

Your role

As a Machine Learning Engineer at Exnaton, you will join our 25-person team and shape the company and its vision from the ground up. Among others, your main responsibilities are the following:

• Contributing features to a state-of-the-art application for the energy sector and proposing new data-driven features that derive value from customers’ energy data (mostly time-series data);

• Developing state-of-the-art machine-learning models for forecasting energy loads and prices, pattern recognition, and anomaly detection in energy data;

• Contributing to our data pipelines and deployment setup for our models;

• Collaborating closely with our software engineers to bridge the gap between the backend and analytical core of our product;

• As we are planning to grow the team in the next few years, you will ideally support and mentor new colleagues and review the team’s models, code, and documentation in the future.

Your qualifications

We define ourselves by a culture of collaboration and ownership. We’re looking for a driven, capable, and thoughtful person to add to our mission. We are looking for someone who:

• Holds a degree in statistics, mathematics, physics, computer science or related field;

• In-depth understanding of mathematical and statistical concepts behind most common machine learning techniques and time-series data;

• Experience in coding and working with data using a wide range of tools and frameworks: Python, Pytorch, Tensorflow, Keras, Docker;

• Ideally experience with cloud providers like AWS and frameworks like Docker and Kubernetes;

• Strong analytical and problem-solving skills, ideally in one or more of the following areas: machine learning for structured data, interpretable AI, uncertainty estimation, representation learning, or Bayesian deep learning;

• Ability to see solutions and opportunities that others do not see;

• Is a team player and thrives in a dynamic startup environment.

We would consider it a big plus if, besides working with data, you have an interest in renewable energy and sustainability. If you do not meet all of the above requirements but feel you would be a good match for our team do not hesitate to apply anyway.

Benefits

At Exnaton, you work in a dynamic environment with a flat hierarchy and direct communication, fostering quick operation and collaborative learning. You can also expect the following:

• Competitive salary and equity package;

• Flexible working hours and location as we believe in ownership and trust. We are a remote-first company and we support those working from different locations;

• Additional benefits such as a new MacBook, a learning budget, sports membership, or Headspace subscription;

• Virtual office for a truly interactive collaboration, even in a distributed team;

• Working with exciting technology for a really important purpose, together with a highly motivated team.