AI Performance Engineer Network Analysis KSA

Company

Qualcomm Middle East Information Technology Company LLC

Engineering Group >
Systems Engineering

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 s 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.

Qualcomm is utilizing its traditional strengths in digital wireless technologies to play a central role in the evolution of Cloud AI. We are hiring AI Performance Engineers at multiple levels to join our dynamic, collaborative team.

• Convert, optimize and deploy models for efficient inference using PyTorch and ONNX.
• Work at the forefront of GenAI by understanding advanced algorithms (e.g., attention mechanisms, MoEs) and numerics to identify new optimization opportunities.
• Perform performance analysis and optimization of LLM, VLM, and diffusion models for inference, scaling performance for throughput and latency constraints.
• Map the next‑generation AI workloads on top of current and future hardware designs.
• Work closely with customers to drive solutions by collaborating with internal compiler, firmware, and platform teams.
• Analyze complex performance or stability issues to identify root causes and propose solutions.
• Create engineering solutions to deliver continuous insights into performance of AI workloads guiding improvements over time.
• Design and implement high‑level kernels, e.g., in Triton, focusing on generating efficient, low‑level code.

• Hands‑on experience building and optimizing language models, notably in PyTorch and ONNX, preferably in production‑grade environments.
• Deep understanding of transformer architectures, attention mechanisms, and performance trade‑offs.
• Experience with workload‑mapping strategies exhibiting sharding or various parallelisms.
• Strong Python programming skills.
• Proactive learning about the latest inference optimization techniques.
• Understanding of computer architecture, ML accelerators, in‑memory processing, and distributed systems.
• Strong communication, problem‑solving skills and ability to learn and work effectively in a fast‑paced and collaborative environment.
• MS in Computer Science, Machine Learning, Computer Engineering, or Electrical Engineering.

• Background in neural network operators and mathematical operations, including linear algebra and math libraries.
• Understanding of machine learning compilers.
• Experience in converging accuracy and its evaluation methods.
• Knowledge of torch.compile or torch
  
  Dynamo.
• PhD in Computer Science, Computer Engineering, or Machine Learning.

Apart from working with great people we offer the following:

• Salary including housing and transport allowance.
• Stock (RSU's) and performance‑related bonus.
• 16 weeks fully paid maternity leave.
• 6 weeks fully paid paternity leave.
• Employee stock purchase scheme.
• Child education allowance.
• Relocation and immigration support (if needed).
• Life and medical insurance.
• Live+Well reimbursement for health and recreational membership fees.

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.
• Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.
• PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.

Qualcomm is an equal‑opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, Qualcomm is committed to providing an accessible process. Contact disability‑ or call the designated toll‑free number for assistance.