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ML Platform Software Engineer

Pubblicato il 20-08-2026 - Qualcomm in Roma

Company:
Qualcomm Europe, Inc. Italy Branch Office

Job Area:
Engineering Group, Engineering Group > Software Engineering

General Summary:
We are seeking an exceptional
Staff Software Engineer
to join our ML Platform team. This role is ideal for a highly technical individual contributor who combines deep hands-on expertise with the ability to lead and drive complex projects independently. You will design, build, and optimize large-scale ML and data infrastructure across on-premises NVIDIA DGX clusters and AWS Cloud, enabling advanced training pipelines, robust data workflows, and production-ready ML solutions.

Beyond infrastructure, this position requires
strong software engineering fundamentals
. You will be expected to write high-quality, production-grade code and master common programming languages. The ideal candidate is passionate about building scalable systems and can balance platform engineering with solid software development practices.

Minimum Qualifications:
- Bachelor's degree in Engineering, Information Systems, Computer Science,



or related field and 4+ years of Software Engineering or related work experience.

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
- 2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.

Key Responsibilities
- Architect and develop core components of the ML platform and data infrastructure for training, inference, and large-scale data processing.
- Design and implement scalable solutions for GPU clusters and distributed data pipelines on-prem and in AWS.
- Lead project workstreams, ensuring timely delivery and alignment with platform roadmap; operate independently and drive outcomes end-to-end.
- Build and optimize data pipelines for inges

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