AI role · Open offer

ML Engineer — e-commerce search

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bol

Full-time
Senior
Search ML
Hybrid
Papendorpseweg 30, 3528 BJ Utrecht, Netherlands · Hybrid
EUR 78k–102k/year

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: Papendorpseweg 30, 3528 BJ Utrecht, Netherlands · Hybrid
Compensation: EUR 78k–102k/year
Experience level: Senior
Team: Search ML
Benefits & perks

About this role

What the team is looking for and how this position fits.

bol is hiring a Senior ML Engineer — e-commerce search on the Search ML team (Hybrid · Utrecht, Netherlands). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation 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.

bol is the Benelux e-commerce leader investing in semantic search and personalization.

Search ML engineers improve embeddings, ranking, and relevance metrics.

What you will do

Ship production e-commerce search ML with offline evals, monitoring, and safe rollout.
Work with product and domain experts on requirements and trade-offs.
Improve data pipelines, labeling, and metrics tied to business KPIs.
Document runbooks for on-call and customer-facing teams.

What we look for

Strong Python and modern ML/LLM stack with shipped features.
SQL, experimentation, and clear communication to stakeholders.
Eligible to work in the listed country; professional English required.

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.).

Vector DBsTool
Embedding storage, hybrid search, and freshness strategies for RAG and semantic retrieval.

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: Semantic search or ranking with offline/online eval.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, and Vector DBs.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Utrecht, Netherlands)—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.
You read the full listing (comp: EUR 78k–102k/year, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Semantic search or ranking with offline/online eval.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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