Company Overview
Our client is a Research lead organisation focusing on cutting-edge software, AI, and hardware innovation.
Position Overview
System Software Manager to lead development of an efficient on-device AI software stack. This role focuses on high-performance local inference, agentic workloads, low latency, efficient memory use, scalable infrastructure, practical deployment on resource-constrained platforms, and delivering a streamlined out-of-box experience for developers and end users.
Responsibilities
- Lead and grow a team building the on-device AI inference platform with accountability for execution, technical direction, delivery quality, and roadmap alignment.
- Drive cross-functional alignment with software, research, architecture, and product teams, along with industry partners and open-source communities, to build strategy and strengthen the AI ecosystem.
- Provide technical leadership for the architecture and evolution of modern inference runtimes and execution stacks across frameworks such as Llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT-RTX, spanning workloads including LLMs, vision-language models, TTS, ASR, and diffusion models.
- Mentor engineers, develop technical leaders, and foster a high-performance team culture centred on innovation, collaboration, and operational excellence.
- Coordinate end-to-end optimization of AI models, data pipelines, and inference runtimes to improve performance across current and next-generation GPU architectures.
Skills & Experience
- 12+ overall years of industry experience and 2+ years of engineering leadership experience, combined with a Bachelor's, Master's, or PhD in Computer Science, Software Engineering, Mathematics, or a related field.
- Proven experience leading high-performing engineering teams in systems software, AI infrastructure, inference runtimes, or related domains.
- Strong technical foundation in C++ software development, debugging, data structures, algorithms, and machine learning systems.
- Extensive background in AI inference pipelines and Deep Learning frameworks like Llama.cpp, vLLM, PyTorch, WinML, DXCGC, and TensorRT.
