Senior ML Engineer, Personalisation & Recommendations

asos.com Ltd; Greater London; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

25 days annual leave

Discretionary bonus

Private medical care

Flexible benefits

Personalised learning

Sample sales

Job summary

ASOS is seeking a Senior Machine Learning Engineer to join the Search & Recommendations group. You will design and operate large-scale ML systems powering product discovery, recommendations and personalization for millions of customers.

You’ll collaborate with scientists, engineers and product managers to deploy impactful solutions at scale. You will lead ML initiatives, contribute to the technical roadmap, and mentor other engineers while advancing production-grade ML capabilities in a

Qualifications

• Experience applying ML and DL techniques in production.
• Familiarity with distributed training and GPU-based environments.
• Strong software engineering and MLOps practices.

Responsibilities

• Design, build and operate production ML systems at scale.
• Collaborate with scientists, engineers and PMs to deploy solutions.
• Develop and deploy batch and real-time ML models in production.
• Improve recommendation, ranking and personalization capabilities.
• Contribute to ML standards and best practices across teams.
• Mentor engineers through coaching and knowledge sharing.
• Help shape technical direction of the ML community.

Skills

ML in production

Deep learning

Distributed computing

MLOps

Technical leadership

Cross-functional collaboration

Tools

TensorFlow

PyTorch

Spark

Job description

ASOS is seeking a Senior Machine Learning Engineer to join the Search & Recommendations group. You will design and operate large-scale ML systems powering product discovery, recommendations and personalization for millions of customers.

You’ll collaborate with scientists, engineers and product managers to deploy impactful solutions at scale. You will lead ML initiatives, contribute to the technical roadmap, and mentor other engineers while advancing production-grade ML capabilities in a