Cloud AI Performance Engineer

Qualcomm; Ireland; On-site; EUR 90,000 - 150,000; Full time

Benefits offered by this job

Stock bonus

Employee stock purchase scheme

Pension matching scheme

Education assistance

Relocation and immigration support

Life and health insurance

Well-being memberships

Job summary

QT Technologies Ireland Limited in Cork, Ireland is hiring an AI Performance Engineer to optimize models for inference, spanning PyTorch, ONNX, and transformer-based workloads. The role covers full lifecycle from research to deployment and requires collaboration with compiler, firmware and platform teams.

The candidate will analyse complex performance issues, map AI workloads to hardware, and develop high-level kernels (e.g., Triton) for efficient low-level code.

Qualifications

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field required; 4+ years software engineering experience.
• Master's degree in Engineering, Information Systems, Computer Science, or related field; 3+ years software engineering experience.
• PhD in Engineering, Information Systems, Computer Science, or related field; 2+ years software engineering experience.
• 2+ years of work experience with programming languages such as C, C++, Java, Python, etc.

Skills

C

C++

Java

Python

Education

Bachelor's degree in Engineering, Information Systems, Computer Science, or related field

Master's degree in Engineering, Information Systems, Computer Science, or related field

PhD in Engineering, Information Systems, Computer Science, or related field

Job description

QT Technologies Ireland Limited in Cork, Ireland is hiring an AI Performance Engineer to optimize models for inference, spanning PyTorch, ONNX, and transformer-based workloads. The role covers full lifecycle from research to deployment and requires collaboration with compiler, firmware and platform teams.

The candidate will analyse complex performance issues, map AI workloads to hardware, and develop high-level kernels (e.g., Triton) for efficient low-level code.