Model Accuracy Development and Test Engineer, Senior (Datacentre AI Engineering) - Riyadh, KSA

<div><p><b>Company:</b>Qualcomm Middle East Information Technology Company LLC</p><p><b>Job Area:</b>Engineering Group, Engineering Group>Software Engineering</p><h3>General Summary</h3><h3>About Us</h3><p>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.</p><h3>About the role</h3><p>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.</p><h3>Key Responsibilities</h3><ul><li>Define and implement accuracy KPIs across precision modes</li><li>Develop scalable Python-based accuracy evaluation tools and automated pipelines.</li><li>Implement accuracy‑preserving optimizations for inference frameworks (TensorRT, ONNX Runtime, AITemplate, Triton).</li><li>Build and maintain automated pipelines for accuracy evaluation across multiple frameworks (ONNX, TensorFlow, PyTorch).</li><li>Develop reusable plugins for preprocessing, post‑processing, and metric evaluation.</li><li>Execute comprehensive accuracy tests for large‑scale models (LLMs, vision, diffusion).</li><li>Validate accuracy under various quantization and precision settings (FP32, FP16, INT8).</li><li>Perform accuracy analysis with deep understanding of model architecture, including layers, attention mechanisms, and parameter configurations.</li><li>Identify architecture‑driven accuracy degradation trends and propose optimization strategies.</li><li>Identify issues related to preprocessing drift, tokenization mismatches, operator fallback, and quantization effects.</li><li>Analyze accuracy differences across hardware targets, firmware versions, and runtime backends.</li><li>Perform slice‑based accuracy analysis (batch size, concurrency, sequence length, domain shifts).</li><li>Design and run experiments to recover accuracy, including fine‑tuning, calibration, and hyperparameter adjustments.</li><li>Debug accuracy failures by tracing root causes across data preprocessing, model layers, quantization steps, and deployment pipelines.</li><li>Compare results across different hardware/software stacks and generate actionable insights.</li><li>Document workflows, maintain dashboards, and publish accuracy results for stakeholders.</li></ul><h3>Required Skillset</h3><ul><li>Strong background in AI/ML model evaluation and accuracy metrics.</li><li>Solid understanding of model architectures (transformers, CNNs, RNNs, MoE) and their impact on accuracy.</li><li>Experience with large language models (LLMs) and generative AI accuracy validation.</li><li>Expertise with inference runtimes (TensorRT, ONNX Runtime, Triton).</li><li>Understanding of quantization (INT8/FP8/INT4), calibration, QAT, and accuracy trade‑offs.</li><li>Experience with model graph conversion (PyTorch → ONNX → backend engines).</li><li>Hands‑on experience with accuracy pipeline development and automation frameworks.</li><li>Proficiency in Python and familiarity with ML toolkits (ONNX Runtime, TensorFlow, PyTorch).</li><li>Expertise in accuracy analysis, including statistical methods and visualization tools.</li><li>Ability to design experiments for accuracy recovery and debug accuracy failures effectively.</li><li>Knowledge of quantization techniques and mixed‑precision workflows.</li><li>Strong problem‑solving and analytical skills with the ability to isolate complex accuracy issues.</li></ul><h3>Preferred Skillset</h3><ul><li>Understanding of video generation model accuracy and multi‑modal evaluation benchmarking</li><li>Experience with data‑center accelerators (NVIDIA A100/H100/B200, AI100 Ultra, Gaudi, TPU).</li><li>Knowledge of LLM accuracy evaluation tools (lm‑eval, HELM, synthetic benchmarks) is an advantage</li><li>Familiarity with distributed deployment systems (Kubernetes, cloud inference services).</li></ul><h3>Qualifications</h3><ul><li>Bachelor's / Masters degree in Engineering, Machine learning/ AI, Information Systems, Computer Science, or related field.</li><li>4-10 years of Software Engineering or related work experience.</li><li>4-10 years of experience with Programming Language such as C, C++, Python.</li></ul><h3>What's on Offer</h3><ul><li>Salary including housing&transport allowance</li><li>Stock (RSU's) and performance related bonus</li><li