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AI role · Open offer

Senior ML Engineer — search & observability AI

Elastic

Full-time
Senior
Elastic AI
Hybrid
Keizersgracht 281, 1016 ED Amsterdam, Netherlands · Hybrid
EUR 85k–120k/year + equity

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: Keizersgracht 281, 1016 ED Amsterdam, Netherlands · Hybrid
Compensation: EUR 85k–120k/year + equity
Experience level: Senior
Team: Elastic AI
Benefits & perks: RSUs, hybrid Amsterdam, learning stipend.
How to apply: Search ranking, log anomaly detection, or RAG on Elasticsearch.
Company site: www.elastic.co
Listed: Jul 30, 2026
Updated: Aug 3, 2026

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.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
ElasticsearchTool
Regular use of Elasticsearch in the LLM & generative AI stack—configuration, debugging, and team conventions.

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, LangChain, PyTorch, SQL, and Kubernetes.
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.
You read the full listing (comp: EUR 85k–120k/year + equity, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on My account, and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

Search ranking, log anomaly detection, or RAG on Elasticsearch.

Sign in with a candidate account to apply

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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