LLM Serving Engineer; Cloud AI Engineering KSA

Company

Qualcomm Middle East Information Technology Company LLC

Engineering Group, Engineering Group >
Systems Engineering

About UsQualcomm 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 investing in several supporting technologies including Deep Learning. The Qualcomm Cloud AI team is developing hardware and software solutions for Inference Acceleration.

We are hiring LLM Serving Engineers at multiple levels to join our dynamic, collaborative team.

This role spans the full product lifecycle—from cutting‑edge research and development to commercial deployment and demands strategic thinking, strong execution, and excellent communication skills.

• Building a scalable LLM inference platform using inference techniques (e.g. disaggregated serving and KV‑Cache management, advanced parallelism, speculative algorithms, model optimization, specialized kernels).
• Contribute to the development of LLM Serving packages (e.g. vLLM, SGLang, TGI, Triton‑Inference server, Dynamo, LLM‑d).
• Work closely with customers to drive solutions by collaborating with internal compiler, firmware and platform teams.
• Work at the forefront of GenAI by understanding advanced algorithms (e.g. attention mechanisms, MoEs) and numerics to identify new optimization opportunities.
• Drive efficient serving through smart autoscaling, load balancing and routing.
• Engage with open‑source serving communities to evolve the framework.

• Hands‑on experience in one or more of the following LLM serving/Orchestration packages (Triton‑Inference Server, vLLM, SGLang, Ollama, llm‑d, KServe, LMCache, Moon Cake).
• Deep understanding of foundational LLMs, VLMs, SLMs, transformer‑based architectures.
• Strong experience in developing language models using PyTorch.
• Strong computer science fundamentals – algorithms, data structures, parallel and distributed programming.
• Understanding of computer architecture, ML accelerators, in‑memory processing and distributed systems.
• Strong Python development skills for large‑scale projects with passion for software engineering.
• Experience in analyzing, profiling, and optimizing deep learning workloads.
• Proactive learning about the latest inference optimization techniques.
• Excellent communication and problem‑solving skills, with the ability to thrive in a fast‑paced and collaborative environment.
• MS in Computer Science, Machine Learning, Computer Engineering or Electrical Engineering.

• Open‑source contribution to any GenAI package.
• Experience architecting and developing large‑scale distributed systems.
• High‑level kernel design experience (PyTorch, CUDA, Triton).
• Knowledge of torch.compile or torch
  
  Dynamo.
• PhD in Computer Science, Computer Engineering or Machine Learning.

• Bachelor's degree in Computer Science, Electrical or Computer Engineering, Information Systems, or related field and 5+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• Master's degree in Computer Science, Electrical or Computer Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• PhD in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

• Salary including housing & transport allowance.
• Stock (RSU's) and performance related bonus.
• 16 weeks fully paid Maternity Leave.
• 6 weeks fully paid Paternity Leave.
• Employee stock purchase…