About Hyper

Hyper is an innovative, London-based startup on a mission to redefine how people experience the real world. We’ve built the most accurate and scalable indoor location technology, and have rolled out in IKEA stores across Europe.

We’ve entered a space with huge commercial demand, but previously defined by inaccurate and disappointing solutions, such as bluetooth beacons. Using our new approach, powered by a mix between Augmented Reality sensors and WiFi, we’ve been able to achieve 5x more accurate location than any other solution, with unlimited scale and an amazing user experience.

This is “Indoor location that actually works”.

Check out a demo of our user experience:

The Role

Our mapping methodology encompasses the transformation of CAD floor plans into dynamic digital renditions, enriched with interactive elements facilitating point-of-interest identification and seamless navigation. We're on the lookout for a skilled ML Solutions Engineer to spearhead our mapping R&D division as a foundational team member. Your role will involve pioneering novel solutions aimed at revolutionising our automated mapping capabilities for scalability. Through your innovations, we aim to streamline our store mapping process to mere minutes, enabling us to efficiently scale across tens of thousands of locations.

Responsibilities

• Strong problem-solving and algorithmic skills and familiarity with machine learning and AI techniques applicable to mapping digitisation and automation, with the ability to work in a fast-paced, dynamic startup environment.

• Collaborate with cross-functional teams to understand requirements and translate them into machine learning solutions for mapping digitisation and automation.

• Research, develop and implement machine learning models and algorithms to add value around scalability, automation, localisation, mapping, trajectory prediction, and user behaviour analysis.
  

• Integrate machine learning functionalities into our web-based navigation platform, ensuring seamless compatibility and performance optimisation.

• Design and conduct experiments to evaluate the performance of machine learning models, iterating on solutions to improve accuracy and robustness.

• Work closely with software engineers to deploy machine learning models in production environments, ensuring scalability, reliability, and maintainability.

• Excellent leadership, communication and collaboration skills.

• Stay updated on the latest advancements in machine learning research and technologies, proactively exploring opportunities for innovation and improvement.

Qualifications:

• 3+ years or demonstrable experience in software automation creating machine learning models.

• Strong understanding of machine learning algorithms, including supervised and unsupervised learning, experience with LLM’s, reinforcement learning and deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers.

• Expertise in Computer Vision techniques is crucial for processing indoor images or video feeds for map digitisation and automation purposes. This includes object detection, segmentation, and tracking.

• Proficiency in programming languages such as Python, JavaScript, and relevant libraries/frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and experience with version control systems (e.g., Git) and software engineering best practices is also important for maintaining scalable and maintainable codebases.

• Experience with web development technologies (HTML, CSS, React.js, Node.js) and building web-based applications.

Also great to have:

• Knowledge of navigation and localisation systems, such as SLAM (Simultaneous Localisation and Mapping).

• Familiarity with indoor positioning systems (IPS), Bluetooth Low Energy (BLE) beacons, or Wi-Fi fingerprinting technologies.

• Experience with geospatial data analysis, sensor fusion techniques, or computer vision algorithms.

• Knowledge of cloud computing platforms (e.g., AWS, Google Cloud) and experience with deploying machine learning models in cloud environments.

Working with us

• Based in London - we meet once a week on Wednesdays to collaborate, though this could increase

• Home office allowance and a company MBP

• We have a global WeWork membership, allowing unlimited access to any WeWork location

• Generous equity

• Private healthcare with Vitality

Department

Platform

Locations

London

Remote status

Hybrid Remote