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Help us make circularity real for the world’s largest brands
Pentatonic helps the world’s biggest brands change to become part of the circular economy.
From sourcing novel, bio-based materials and designing new products, business models and supply chains to creating and running takeback and recycling programs, Pentatonic is connecting all these areas for many of our customers.
Our unique technology and unrivalled multidisciplinary team is unlocking real-world circularity, at scale, for global businesses.
Join us and make a significant impact for our world.
About the role
You’ll be spearheading the application of Visual AI models to help the world’s largest brands shift to the circular economy and become more sustainable. You’ll be working with an experienced team of developers and subject matter experts. The tech team is remote (based in the UK & Europe) and the sustainability team are based in our offices in London & Berlin.
You’ll be expected to travel to London or surroundings on a monthly basis to team on-sites, workshops and/or demos.
Responsibilities
• Develop and implement state-of-the-art computer vision algorithms and models for real-time image and video analysis.
• Collaborate with cross-functional teams to integrate computer vision solutions into our products and services.
• Stay up to date with the latest advancements in computer vision and machine learning research and apply them to our solutions.
• As a full stack MLE develop necessary backend infrastructure to efficiently serve Your models.
• Design and oversee data collection pipelines for model training
Requirements
This position is perfect for you, if you’ve got:
• Master's or Ph.D. in Computer Science, Mathematics or related field.
• Proven experience in designing and implementing computer vision algorithms and models.
• Proficient in Python Data Science stack: Python, Numpy, Pandas, Scikit-learn
• Working knowledge of SQL. Basic knowledge of SQLAlchemy ORM is a plus
• Strong knowledge of deep learning frameworks, such as TensorFlow or PyTorch.
• Some experience with AWS and preferably AWS Sagemaker, Lambda and ECS.
• Basic knowledge of Docker
• Experience with image and video processing techniques and libraries.
• Solid understanding of machine learning concepts, including supervised and unsupervised learning, feature engineering, and model evaluation.
• Excellent problem-solving and analytical skills.
• Effective communication and collaboration skills.
• Publications related to machine learning in academic journals is a plus
• Availability for cross team time overlap (9am-6pm GMT/BST) and making a daily stand-up call
