Staff Engineer – GPU Driver & System Software

Category

Chip Design

Location

Cambourne

Experience

More than 4 Years Work Expe.

Job Description

MediaTek Incorporated is a global fabless semiconductor company that enables nearly 2 billion connected devices a year. We are a market leader in developing innovative systems‑on‑chip (SoC) for mobile devices, home entertainment, connectivity and IoT products. MediaTek is the number one Wi Fi supplier across broadband, retail routers, consumer electronics devices and gaming, and its Wi Fi 6 chipsets are powering the latest networking equipment for faster computing experiences. With market leader position of MediaTek SoC design, MediaTek GPU IP team is committed for industry leading, feature rich and PPA competitive GFX IP optimization, customization and development. The GFX IP will be not only deployed in flagship and mainstream mobile SoC, but also served as fundamental technology for adjacent markets like laptop, IoT, AI, VR/AR, automotive etc. The team is now hiring all areas of GPU talents in all levels including Architecture, Architecture, SW driver, Compiler, Performance, Power and Model engineers to join Cambourne, Cambridge, UK. Bring your GPU driver expertise and play a key role in shaping the future of next‑generation GPU technologies. In this role, you will collaborate closely with Architecture, Micro‑architecture, Compiler, and Model teams to unlock maximum performance from our silicon and drive cutting‑edge GPU innovation. This is a full‑stack GPU software opportunity, offering exposure across the entire stack—from industry‑standard graphics and compute APIs to kernel‑mode drivers, GPU firmware, and performance optimization. You will contribute to hardware/software co‑design efforts, ensuring seamless integration and optimal performance across all layers. If you are passionate about low‑level systems, performance tuning, and pushing the limits of GPU capabilities, this role offers a unique platform to make a direct impact on advanced GPU design and development.

Roles And Responsibilities

• API Layer: Architect, develop, and optimize features for industry‑standard APIs including Vulkan, DirectX, OpenGL ES, and OpenCL.
• Kernel & Firmware: Work deep in kernel‑mode drivers and GPU firmware to improve hardware control, stability, scheduling, memory management, and execution efficiency.
• Performance: Identify, analyze, and eliminate bottlenecks across the stack — including driver overhead, memory bandwidth, GPU scheduling, compute throughput, and power/performance tradeoffs.
• Architecture Collaboration: Partner closely with Architecture, Micro‑architecture, Compiler, and GPU Model teams to influence next‑generation GPU design and ensure the software stack fully leverages the hardware.
• Technical Leadership: Lead complex debug efforts, guide design decisions, mentor engineers, and help raise the technical bar across the GPU driver team.
• No Silos: You are not restricted to only the API layer, kernel driver, firmware, or performance. You can work across the entire GPU software stack.
• Shape the Hardware: Your software insights will directly influence next‑generation GPU architecture and micro‑architecture.
• High‑Impact Work: Your contributions will help turn silicon capability into real‑world graphics, compute, and AI performance.

Requirements

• Experience of building GPU drivers, graphics systems, or low‑level systems software.
• You are fluent in C/C++ and comfortable working close to hardware.
• You have deep expertise in one or more industry‑standard APIs such as Vulkan, DirectX 12, OpenCL, or OpenGL ES.
• You understand GPU internals at a deep level, including memory hierarchies, command submission, scheduling, synchronization, pipeline architecture, and performance bottlenecks.
• You enjoy working across boundaries — from API design to kernel drivers, firmware, compiler interaction, and hardware architecture.

Preferred Qualifications (Nice to Have)

• Background in GPU performance analysis, firmware, virtualization, compiler, or runtime optimization for GPUs or accelerators.
• Familiarity with ML/AI workloads, DNN operators and their mapping onto GPU or accelerator architectures.
• Experience collaborating with architects, micro‑architects, compiler, and model teams.