AI tech internship: Smart home AI

Energy-Based Machine Learning for Smart Buildings

Are you passionate about artificial intelligence (AI) and excited to apply deep learning in real-world IoT environments?

This internship offers the opportunity to work at the intersection of energy analytics, artificial intelligence, and embedded-oriented machine learning.

Apply now for this internship!

About this internship

Crownstone devices provide high-frequency electrical measurements that can reveal which appliances are currently in use.

Accurate device identification unlocks powerful smart-home functionality, such as energy monitoring, automation, and safety features.

This internship will focus on developing and optimizing a neural network capable of classifying devices based on real-time power usage signatures.

You will work across the full lifecycle of a NN solution: from data acquisition to model compression and benchmarking, ensuring the network is robust, efficient, and ready for deployment.

What will you do and learn?

This internship combines hands-on machine learning engineering with applied IoT research.

You will:

Collect and annotate high-frequency power data to expand the existing dataset.
Perform feature engineering to extract informative characteristics from electrical signals.
Design, train, and validate neural networks for device classification.
Apply advanced optimization techniques such as pruning, model compression, and quantization to reduce resource consumption.
Benchmark performance against current solutions and iterate toward real-time efficiency.
Gain practical experience working with real hardware in a smart-building environment.

About you

We do not expect you to master all topics yet.

Enthusiasm for applied AI is what matters most.

You are likely a great fit if you:

Study Computer Science, Artificial Intelligence, Electrical Engineering, Applied Physics, or a related field.
Are in the final phase of your bachelor’s or pursuing a master’s degree.
Have solid programming skills in Python and familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch).
Have an analytical mindset and enjoy experimenting with models and data.

It’s a bonus if you:

Have familiarity with signal processing, especially related to energy or sensor data.
Have experience with NN optimization (quantization, pruning, or embedded ML concepts).
Have participated in research projects or hackathons.

If you want to help shape the future of intelligent energy awareness in buildings, this internship gives you a chance to create technology that enters people’s daily lives.

What can you expect from us

About Almende and Crowntone

This internship is at one of our daughter companies, namely Crownstone.

Crownstone is revolutionizing indoor location tracking, transforming how businesses and technologies interact with physical spaces.

Our technology is used for indoor localization, enabling the tracking of various devices like tags, smartphones, and other BLE devices.

Our culture

We are a small-scale and flat organization. The culture is informal, and communication lines are short. We don’t like office politics.

At Almende and Crownstone, employees are given the space to work independently. We empower our employees to voice their ideas. If you enjoy taking initiative, have many ideas, and wish to execute them, you’ll feel right at home.

What we offer

Real hands-on experience: Apply what you’ve learned in your studies to real-life situations
A peek into the international tech scene
Build a network with industry pros
Guidance and mentorship from people who’ve been in your shoes

The application process

Apply

Send us your resume at teresa@crownstone.rocks

First meeting

We’ll first check if your profile fits. If yes, you’ll meet with the Head of the department.

Second meeting

Meet one of your future colleagues for an in-depth talk about the subject matter you applied for.

Offer

If there’s a mutual connection, we’ll make you a great offer. Hopefully, we can welcome you to our team soon!