IoT Systems Engineer – Security & Applied AI

IoT Systems Engineer – Security & Applied AI At Mulini SRL we are looking for a talented and motivated IoT Research/Software Engineer to join our team. In this role, you will bridge the gap between cutting‑edge research and real‑world systems, working at the intersection of systems engineering, security, and applied AI for the Internet of Things. You will design, build, and evaluate robust systems that push the boundaries of what’s possible while maintaining a strong security posture. See mulini.eu for our current projects and products.

Responsibilities

Design and develop scalable, reliable systems that incorporate AI models into production environments.

Identify, analyze, and mitigate security vulnerabilities and mitigation strategies in IoT pipelines, related AI models, and infrastructure.

Collaborate cross‑functionally with research scientists, software engineers, and product teams.

Contribute to threat modeling and security reviews for IoT systems.

Publish findings, write technical reports, and present work to internal and external stakeholders.

Stay current with the latest developments in IoT security, adversarial machine learning, and security research.

What We Offer

Opportunity to work at a high‑impact, cutting‑edge startup.

Collaborative and intellectually stimulating environment.

Competitive salary, equity, and benefits.

Flexible work arrangements (Turin Italy/Hybrid).

Conference attendance and publication support.

We are an equal‑opportunity employer and welcome applicants from all backgrounds.

Required Qualifications

B.S./M.S./Ph.D. in Computer Science, Electrical Engineering, or a related field.

Strong foundation in systems engineering principles (distributed systems, OS internals, networking).

Solid understanding of security principles – network security, IoT security, threat modeling, and vulnerability analysis.

Proficiency in Python and at least one systems‑level language (C, C++, Rust, or Go).

Preferred Qualifications

Experience with adversarial machine learning, model robustness, or AI safety.

Experience deploying and maintaining ML models in production.

Hands‑on experience with applied ML frameworks (e.g., PyTorch, TensorFlow, JAX).

Prior research experience with publications in top‑tier venues (NDSS, USENIX, IEEE S&P, CCS, etc.).

Experience with cloud security (AWS, GCP, or Azure) and containerized environments (Docker, Kubernetes).

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