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Senior ML Engineer — maps & mobility

TomTom

Full-time
Senior
Location AI
Hybrid
De Ruijterkade 154, 1011 AC Amsterdam, Netherlands · Hybrid
EUR 82k–110k/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: De Ruijterkade 154, 1011 AC Amsterdam, Netherlands · Hybrid
Compensation: EUR 82k–110k/year
Experience level: Senior
Team: Location AI
Benefits & perks: Hybrid, mapping data, automotive partners.
How to apply: Geospatial ML or traffic prediction with map constraints.
Company site: www.tomtom.com
Listed: Jun 29, 2026
Updated: Aug 3, 2026

About this role

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

TomTom is hiring a Senior Senior ML Engineer — maps & mobility on the Location 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.

TomTom provides map, traffic, and location intelligence for automotive and mobility partners.

ML engineers improve geospatial models, traffic prediction, and map-matching quality.

What you will do

Ship production maps and mobility 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.
Computer Vision
Image or video models with clear eval harnesses (precision/recall, calibration, or domain-specific scores).

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.

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: Geospatial ML or traffic prediction with map constraints.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, Computer Vision, and Spark.
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 82k–110k/year, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Geospatial ML or traffic prediction with map constraints.

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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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