Contract Type: 6 month contract outsourced via agency on an hourly rate
Location: Egham
Hybrid: 3 days onsite (minimum) and 2 days working from home
Rate: Very much dependant on level of experience.
Key responsibilities include:
- Performance Optimization: Profile and debug performance bottlenecks at the OS, runtime, and model levels.
- Model Deployment: Work across the stack—from model conversion, quantization, and optimization to runtime integration of AI models on-device.
- Toolchain Evaluation: Compare deployment toolchains and runtimes for latency, memory, and accuracy trade-offs.
- Open-Source Contribution: Enhance open-source libraries by adding new features and improving capabilities.
- Experimentation & Analysis: Conduct rigorous experiments and statistical analysis to evaluate algorithms and systems.
- Prototyping: Lead the development of software prototypes and experimental systems with high code quality.
- Collaboration: Work closely with a multidisciplinary team of researchers and engineers to integrate research findings into products.
We not require a PhD holder this time which is unusual for the AI Team.
We're looking for someone with:
- Technical Expertise: Strong OS fundamentals (memory management, multithreading, user/kernel mode interaction) and expertise in ARM CPU architectures.
- Programming Skills: Expert proficiency in Python and Rust, with desirable knowledge in C and C++.
- AI Knowledge: Solid understanding of machine learning and deep learning fundamentals, including architectures and evaluation metrics.
- Problem-Solving: Strong analytical skills and the ability to design and conduct rigorous experiments.
- Team Player: Excellent communication and collaboration skills, with a results-oriented attitude
Desirable Skills:
- Experience with ARM 64-bit architecture and CPU hardware architectures.
- Knowledge of trusted execution environments (confidential computing).
- Hands-on experience with deep learning model optimization (quantization, pruning, distillation).
- Familiarity with lightweight inference runtimes (ExecuTorch, llama.cpp, Candle).
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