GPU Architecture Validation & Performance Intern

Apple Inc.; St Albans; On-site; GBP 18,000 - 26,000; Full time

Job summary

Apple Inc. invites applications for GPU internship positions in Architecture Validation, Performance Modelling and Platform Architecture.

These roles are based in the UK with potential placements in London, St Albans or Cambridge, and offer engaging projects across hardware and software collaboration. Interns will build device-level models, analyse workloads, and help drive next-generation GPU performance and efficiency in collaboration with experienced engineers.

Qualifications

• Currently pursuing a BS, MS, or PhD in Computer Science, Electrical Engineering, Computer Engineering, or a related field.
• At the end of the internship, you must return to school to continue your education or the internship must be the last requirement for you to graduate.
• Strong coding skills with C or C++
• Strong understanding of common data structures, algorithms, and design patterns
• A curiosity about GPU / CPU / SOC architecture and micro-architecture
• Knowledge of scripting languages such as Perl, Python or Ruby

Responsibilities

• Build bit-accurate C/C++ models of the GPU or design workloads to verify new GPU features through models.
• Collaborate with verification and software teams to identify bugs in workloads, models and hardware, and communicate recommendations.
• Define and optimize Apple's GPU micro-architecture through performance modelling and data collection.

Skills

C/C++

Algorithms

GPU architecture

Python

Scripting

Education

BS/MS/PhD in CS/EE

Tools

SystemVerilog/Verilog/VHDL

GPU APIs (Metal, DX12, Vulkan, CUDA, OpenGL, OpenCL)

Job description

Apple Inc. invites applications for GPU internship positions in Architecture Validation, Performance Modelling and Platform Architecture.

These roles are based in the UK with potential placements in London, St Albans or Cambridge, and offer engaging projects across hardware and software collaboration. Interns will build device-level models, analyse workloads, and help drive next-generation GPU performance and efficiency in collaboration with experienced engineers.