Model Accuracy Development and Test Engineer, Senior (Datacentre AI Engineering) - Riyadh, KSA
Qualcomm · Riyadh, Saudi Arabia
About this role
Qualcomm is hiring a senior-level QA Engineer in the software engineering function based in Riyadh, Saudi Arabia. The posting calls out experience with Python, Java, C, Kubernetes.
- Role
- QA Engineer
- Function
- software engineering
- Level
- senior
- Track
- Individual contributor
- Location
- Riyadh, Saudi Arabia
- Posted
- Mar 30, 2026
More roles at Qualcomm
Job description
from Qualcomm careers##
Company:
Qualcomm Middle East Information Technology Company LLC
## Job Area:
Engineering Group, Engineering Group > Software Engineering
General Summary:
##
## About Us
Qualcomm is growing its presence in Riyadh and is hiring Data Centre Engineers to support our expanding infrastructure across the region. As Saudi Arabia accelerates its digital transformation under Vision 2030, Qualcomm is investing in world‑class computing and data centre capabilities to power AI, cloud, and advanced connectivity at scale. This is a unique opportunity to work in a fast‑growing technology hub, supporting critical environments and helping shape the future of data centre operations in the Kingdom and beyond.
About the role:
We are seeking an Inference Accuracy senior engineer to design, develop, and validate model accuracy of deep learning models deployed at scale. The role focuses on deep accuracy analysis, debugging, accuracy evaluation, and recovery during inference on large data-center hardware platforms. This position requires strong problem-solving ability, excellent Python programming skills, and hands-on expertise with inference pipelines.
Key Responsibilities include:
* Define and implement accuracy KPIs across precision modes
* Develop scalable Python-based accuracy evaluation tools and automated pipelines.
* Implement accuracy-preserving optimizations for inference frameworks (TensorRT, ONNX Runtime, AITemplate, Triton).
* Build and maintain automated pipelines for accuracy evaluation across multiple frameworks (ONNX, TensorFlow, PyTorch).