Graduate Embedded AI Engineer - 12 month Fixed Term Contract

Valeo; Tuam; On-site; EUR 32,000 - 52,000; Full time

Job summary

Valeo's Tuam-based team is seeking a Graduate Embedded AI Engineer to work on deployment, integration and validation of AI/ML models on embedded and edge platforms. You will apply software engineering, deep learning and embedded-system fundamentals under guidance from experienced engineers to build production-ready AI solutions.

The role emphasizes building solid technical foundations, learning new tools, and contributing to real-world projects.

Qualifications

• Honours Bachelor's degree or equivalent in a technical discipline.
• Master's degree beneficial but not required.
• Recent graduates welcome with up to 1 year post-graduation experience.
• Strong fundamentals in software engineering and ML concepts.

Responsibilities

• Assist in deploying and validating AI/ML models on embedded/edge platforms.
• Develop software components using C/C++ and Python.
• Test, benchmark and optimise AI solutions for target hardware.
• Build tests and automation scripts for integration.
• Collaborate with senior engineers and stakeholders.

Skills

C/C++

Python

Linux

Git

AI/ML concepts

Embedded systems

Problem solving

Communication

Willingness to learn

Education

Bachelor's degree in CS/EE/CE

Master's degree preferred

Tools

Docker

CMake

Git

Job description

Mission

As part of the anSWer Embedded AI team, the Graduate Embedded AI Engineer works on the deployment, integration and validation of AI/ML models on embedded and edge computing platforms. Working under the guidance of experienced engineers, they apply software engineering, deep learning and embedded-system fundamentals to implement, test, benchmark and optimise AI solutions for target hardware. The role focuses on developing strong technical foundations while contributing to production-oriented engineering projects.

Mission

As part of the anSWer Embedded AI team, the Graduate Embedded AI Engineer works on the deployment, integration and validation of AI/ML models on embedded and edge computing platforms. Working under the guidance of experienced engineers, they apply software engineering, deep learning and embedded-system fundamentals to implement, test, benchmark and optimise AI solutions for target hardware. The role focuses on developing strong technical foundations while contributing to production-oriented engineering projects.

Responsibilities

Embedded AI Deployment

• Assist senior engineers in integrating and deploying trained AI/ML models onto embedded/edge platforms.
• Develop and modify software components using C/C++ and Python.
• AI model conversion, operator compatibility checks, custom operator development and integration with appropriate inference runtimes and hardware platforms.
• Build, run and test software on development boards or target hardware.
• Investigate software/model integration issues with guidance from experienced engineers.
• Develop integration tests and simple automation scripts.

Competencies

• Discipline Design Process Application: Demonstrate proficiency in applying the established Discipline design processes and methodologies.
• Technical Learning: Actively develop knowledge in embedded systems, deep learning inference, AI deployment frameworks, target hardware architectures and software optimisation techniques through project work, mentoring and self-directed learning.

Development Automation and Efficiency

• Use state of art development tools and develop automated processes for software builds, testing, model conversion, deployment and benchmarking.

Innovation

• Contribute ideas, experiments and prototypes to investigate technical problems & potential improvements. Innovation is expected, with technical direction and review provided by experienced engineers.

Collaboration And Influence

• Effective Communication: Communicate effectively with team members and stakeholders, ensuring clear and concise information exchange.

Profile

Education

Honours Bachelor's degree (NFQ Level 8 or equivalent) in Computer Engineering, Electronic/Electrical Engineering, Computer Science, Software Engineering, Artificial Intelligence, Robotics or a related technical discipline.

A Master's degree is beneficial but not required.

Experience

Applications from recent graduates with no commercial engineering experience are welcome. Relevant experience may include internships, university projects, final-year projects, research projects, hackathons, substantial personal/open-source projects, and maximum one year in a company after graduation.

Skills

• Basic understanding of programming and software engineering principles.
• Working knowledge of C or C++ through academic, internship or personal-project experience.
• Working knowledge of Python.
• Familiarity with a Linux development environment.
• Familiarity with Git/version control.
• Basic understanding of machine-learning concepts, including training versus inference, datasets and model evaluation.
• Exposure to at least one ML framework such as PyTorch or TensorFlow.
• Basic understanding of System on Chip (SOC) architecture, embedded systems or constrained computing.
• Good analytical and problem-solving skills.
• Ability to communicate technical information clearly.
• Strong willingness to learn unfamiliar technologies.

Desirable / Nice To Have Skills (not Mandatory)

• ONNX / ONNX Runtime
• TensorFlow Lite
• PyTorch / ExecuTorch
• Model quantisation
• Pruning or model compression
• ARM-based processors
• GPU / NPU / DSP accelerators
• Embedded Linux
• RTOS
• Cross-compilation
• CMake
• Docker
• CI/CD
• Profiling/debugging tools
• SIMD/vectorisation
• Hardware/software interfacing