AI role · Open offer
Senior ML Engineer — fashion discovery
ASOS
This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- Greater London House, Hampstead Road, London NW1 7FB, United Kingdom · Hybrid
- Compensation
- £90k–£125k + bonus
- Experience level
- Senior
- Team
- Personalization
- Benefits & perks
About this role
What the team is looking for and how this position fits.
ASOS is hiring a Senior Senior ML Engineer — fashion discovery on the Personalization team (Hybrid · London, United Kingdom). This is a hands-on role in computer vision & geospatial AI—not a generic “AI enthusiast” post.
You will own vision, sensor, or geospatial models in production end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.
**ASOS** is a global **fashion marketplace** — discovery, visual search, and personalization for millions of style-conscious shoppers from **London**.
Senior ML Engineer (hybrid Camden): ranking, **visual similarity**, and **size-fit intelligence** with conversion and diversity metrics.
About ASOS
- London HQ at Greater London House; digital-first fashion retailer.
- ML drives browse, search, and email personalization.
- Hybrid teams with experimentation culture.
What you will do
- Improve recommendation and search models with A/B discipline.
- Build multimodal embeddings for outfit and visual search.
- Monitor model drift across seasons and trend spikes.
Stack & environment
- Python, PyTorch, Spark, Kubernetes.
- Hybrid London NW1.
What we look for
- E-commerce or recommender systems at scale.
- Right to work in the UK.
Skills we're hiring for
What each capability means for this role—not a keyword checklist.
- Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
- PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
- SQL
Tools & stack
Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).
- SparkTool
Distributed feature generation and batch scoring on large tabular or event datasets.
- KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
You're a strong fit if…
Tailored to this listing—use it as a self-check before you apply.
- ✓You can show evidence in your application: Recommendation or visual search with conversion lift proof.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, Computer Vision, and Spark.
- ✓You have owned vision, sensor, or geospatial models in production in production: debugging live issues, running postmortems, and iterating from real user or business signals.
- ✓You are comfortable with the hybrid rhythm (Hybrid · London, United Kingdom)—onsite collaboration when it matters, deep work when remote.
- ✓You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
- ✓
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What the employer asked for
Recommendation or visual search with conversion lift proof.
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