Machine Learning Engineer

We are looking for a Machine Learning Engineer, with expertise in deep learning, to join our cross-functional AI Trade Optimization team.

You will work alongside data engineers and scientists to solve problems and productionise interesting solutions that leverage cutting edge tech. At ASOS, as an online only retailer, we have unique datasets like transactions and click streams for millions of customers and hundreds of thousands of products.

What you’ll be doing:

• You will be part of an agile, cross-functional team building and improving our causal algorithms for the pricing and customer targeting space.
• You will be working alongside scientists in driving the implementation and deployment of at-scale solutions for our hundreds of millions of customers/products, creating measurable impact across the business.
• You will be deploying batch and online machine learning models at high scale.
• You will be continually developing and improving our code and technology, taking an active role in the conception of brand-new features.
• You will contribute to the team's technical direction, contribute to ML standards, and drive quality across ASOS's ML community, while sharing expertise gained from the team.

About You

• You have professional experience in machine learning with experience in deep learning methods and their practical applications in production environments.
• You have working knowledge of ML frameworks (e.g., PyTorch, TensorFlow) and experience with model deployment.
• You have experience with GPU-based model training, with interest in learning advanced distributed computing techniques and parallelization strategies.
• You have strong understanding of software development lifecycles and engineering practices (Data pipelines, CI/CD, containerisation, observability) - specifically ML Ops principles, techniques and tooling.
• You are a self-starter with a strong desire to learn and grow professionally.
• You have excellent communication skills and enjoy collaborating with diverse teams.