Senior Research Engineer; AI​/ML

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 tool chains 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 (Execu Torch, llama.cpp, Candle).