Job Details

Deep Learning Specialist

IDOVEN is a health tech startup seeking to prevent cardiac disease through AI. With our breakthrough AI-based electrocardiogram analysis platform, we work with physicians and healthcare leaders to enable early detection and diagnosis at scale.

This is a unique opportunity. With the backing of the best investors in the industry, the European Innovation Council (EIC) and Horizon 2020 European Commission, you'll have the responsibility and resources to shape the creation of a game-changing product that can save millions of lives.

Our technology is recognized with the European Seal of Excellence, supported by EIT Health (a network of best-in-class health innovators backed by the EU), and received the Healthy Longevity Catalyst Award from the U.S. National Academy of Medicine. In 2021, we were awarded at South Summit as Most Disruptive Startup and Best Health Startup. We are one of the most promising European technology companies to watch.

About the Role

We are seeking a highly motivated and innovative Deep Learning Specialist to lead the development and optimization of our AI-powered ECG analysis platform. This role demands deep expertise in deep learning modeling, with a proven ability to design and implement custom models from scratch. You will play a crucial part in advancing the state-of-the-art in cardiac diagnostics by exploring and implementing cutting-edge Deep Learning techniques.

Responsibilities:

• Lead and mentor a team of data scientists and engineers, providing technical guidance and fostering professional growth.
• Continuously research and integrate emerging Deep Learning techniques, including representation learning, self-supervised learning, generative models, and novel neural network architectures, to drive innovation.
• Analyze, enhance, and scale Deep Learning models for complex signal processing tasks, ensuring high accuracy, efficiency, and generalization.
• Oversee the implementation of data balancing, augmentation, and synthetic data generation techniques to improve model robustness.
• Drive model optimization efforts, leveraging techniques such as fine-tuning, distillation, quantization, and distributed training for improved performance.
• Spearhead automation initiatives for data preprocessing, model training, and deployment pipelines to accelerate development cycles and ensure efficiency.
• Collaborate closely with engineers, domain experts, and stakeholders to ensure technical solutions align with real-world applications and business goals.
• Advocate for best practices in AI model development, validation, and deployment, ensuring high-quality and scalable solutions.
• Stay abreast of the latest advancements in Deep Learning, signal processing, and AI to maintain a cutting-edge approach to research and development.