Machine Learning Engineer; Datacentre AI Engineering KSA

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Company
Qualcomm Middle East Information Technology Company LLC

Job Area
Engineering Group, Engineering Group >
Software Engineering

General Summary

Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G‑enabled smartphones that double as pro‑level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge.

You’ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.

About The Role

The Qualcomm Cloud Computing team is developing hardware and software for Machine Learning solutions for Data centre AI. We are seeking ambitious, bright, and innovative engineers with experience in machine learning framework development. Job activities span the whole product life cycle from early design to commercial deployment. The environment is fast‑paced and requires cross‑functional interaction. Good communication, planning, and execution skills are a must.

• Analyse software requirements, determine the feasibility of design within the given constraints, consult with architecture and HW engineers, and implement software solutions best suited for Qualcomm’s SOCs.
• Analyse and identify system level issues, interface with the software development, integration, and test teams.
• Lead high‑performance teams towards system design and deliverables.
• Proven track record of leading teams in Machine learning software engineering.
• Strong foundation of mathematical modelling of problems and linear algebra, coupled with state‑of‑the‑art algorithms in ML/AI space.
• Improve and optimise key Deep Learning models on Qualcomm AI 100.
• Build deep learning framework extensions for Qualcomm AI 100 in upstream open‑source repositories.
• Collaborate and interact with internal teams to analyse and optimise training and inference for deep learning.
• Build software tools and ecosystem around AI SW Stack.
• Work on Triton, Execu Torch, Inductor, Torch Dynamo to build abstraction layers for inference accelerator.
• Optimise workloads for both scale‑up (multi‑SoC) and scale‑out (multi‑card) systems.
• Optimise the entire deep learning pipeline including graph compiler integration.
• Apply knowledge of software engineering best practices.

• Deep Learning experience and knowledge – LLMs, Natural Language Processing, Vision, Audio, Diffusion, Recommendation systems.
• Knowledge of the structure and function of different components of Pytorch, Tensor Flow software stacks.
• Excellent C/C++/Python programming and software design skills, including debugging, performance analysis, and test design.
• Ability to work independently, define requirements and scope, and lead your own development effort.
• Well versed with open‑source development practices.
• Strong developer with a research mindset – strives to innovate.
• Avid problem solver – should be able to find solutions to key engineering and domain problems.
• Knowledge of tiling and scheduling a Machine learning operator is a plus.
• Experience in using C++ 14 (advanced features)
• Experience of profiling software and optimisation techniques.
• Hands on experience writing SIMD and/or multi‑threaded high‑performance code is a plus.
• Experience of ML compiler, Auto‑code generation (using MLIR) is a plus.
• Experience in deploying workloads on large scale heterogeneous clusters is a plus.
• Hands‑on experience with CUDA, CUDNN is a plus.

• Bachelor’s / Masters/ PhD degree in Engineering, Machine learning/ AI, Information Systems, Computer Science, or related field.
• 2‑10 years’ of Software Engineering or related work experience.
• 2‑10 years’ experience with Programming Language such as C++, Python.

• Salary including housing & transport allowance
• Stock (RSU's) and performance related bonus
• 16 weeks…