AI role · Open offer
Senior ML Engineer — search & observability AI
Elastic
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At a glance
How you'll work, where, and key listing dates.
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- Keizersgracht 281, 1016 ED Amsterdam, Netherlands · Hybrid
- Compensation
- EUR 85k–120k/year + equity
- Experience level
- Senior
- Team
- Elastic AI
- Benefits & perks
About this role
What the team is looking for and how this position fits.
Elastic is hiring a Senior Senior ML Engineer — search & observability AI on the Elastic AI team (Hybrid · Amsterdam, 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.
**Elastic** builds the **Search AI Platform** — Elasticsearch, observability, and security — with major R&D in **Amsterdam**.
Senior ML Engineer (hybrid Amsterdam): improve **semantic search**, **anomaly detection**, and **RAG** for enterprise observability and security data.
About Elastic
- Amsterdam-listed global software company (NYSE: ESTC).
- ML embedded across Search, Observability, and Security suites.
- Open-source heritage with enterprise compliance requirements.
What you will do
- Train ranking and embedding models on log and document corpora.
- Build eval harnesses for retrieval quality and false-positive rates.
- Partner with platform teams on efficient vector search at scale.
Stack & environment
- Python, PyTorch, Elasticsearch, Kubernetes.
- Hybrid Amsterdam Keizersgracht area.
What we look for
- Search, observability, or security ML experience.
- EU work authorization.
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.).
- ElasticsearchTool
Regular use of Elasticsearch in the LLM & generative AI stack—configuration, debugging, and team conventions.
- 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: Search ranking, log anomaly detection, or RAG on Elasticsearch.
- ✓Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, LangChain, and Elasticsearch.
- ✓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 · Amsterdam, 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.
Apply
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What the employer asked for
Search ranking, log anomaly detection, or RAG on Elasticsearch.
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