Senior ML Research Engineer

Summary: The role of Senior ML Research Engineer involves optimizing performance, deploying AI models, and evaluating toolchains within a hybrid working environment. The position requires collaboration with a multidisciplinary team to integrate research findings into products, while also contributing to open-source libraries. Candidates are expected to have strong technical expertise, programming skills, and a solid understanding of machine learning fundamentals. A PhD is not required for this position, which is atypical for the AI team.

Key Responsibilities:

• 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.

Key Skills:

• 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).

Salary (Rate): undetermined

City: Egham

Country: United Kingdom

Working Arrangements: hybrid

IR35 Status: undetermined

Seniority Level: undetermined

Industry: IT