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AI role · Open offer

ML Engineer — sensor analytics

Leonardo

Full-time
Senior
Defense AI
Hybrid
Piazza Monte Grappa 4, 00195 Rome, Italy · Hybrid
EUR 70k–95k/year

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At a glance

How you'll work, where, and key listing dates.

Employment: Full-time
Workplace: Hybrid
Location: Piazza Monte Grappa 4, 00195 Rome, Italy · Hybrid
Compensation: EUR 70k–95k/year
Experience level: Senior
Team: Defense AI
Benefits & perks: National security context, hybrid, pension.
How to apply: Sensor fusion or anomaly detection (redacted details OK).
Company site: www.leonardo.com
Listed: Jul 14, 2026
Updated: Aug 3, 2026

About this role

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

Leonardo is hiring a Senior ML Engineer — sensor analytics on the Defense AI team (Hybrid · Rome, Italy). 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.

Leonardo develops aerospace, defense, and security systems with growing AI and sensor analytics teams.

ML engineers work on fusion, anomaly detection, and vision in regulated environments.

What you will do

Ship production sensor and defense analytics 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.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

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: Sensor fusion or anomaly detection (redacted details OK).
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, Computer Vision, and Docker.
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 · Rome, Italy)—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 70k–95k/year, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Sensor fusion or anomaly detection (redacted details OK).

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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