Senior Machine Learning Research Engineer - Research Engineering - MSR Cambridge

Working closely with researchers, you'll design, implement and validate proof-of-concept solutions to Machine Intelligence problems, then partner with product teams to land that research in shipping products used by millions of people world-wide. Combine strengths in ML research and software engineering competence to contribute to the design and prioritisation of research activities. Build prototypes of ML systems to demonstrate research value, in some cases bringing these prototypes all the way to product-level readiness. You like getting things done. Evaluate research prototypes, and help write up results to communicate outcomes clearly. Collaborate with researchers and product teams, helping smooth technology transfer between them. Reinforce a positive environment by applying best practices and high-quality engineering standards. Gain deep expertise in one (or more) subareas of research, and general understanding of a broad area. Understand the relevant literature and applicable research techniques. Contribute to academic publication of research outcomes. Proactively ensure high standard of software security over research prototypes and library supply chains. Understand and follow ethics and privacy policies relating to research processes and data handling, as appropriate. Required Master degree in Computer science or related area, or equivalent training and experience in research. Experience with modern ML model architectures in PyTorch. Proficient in collaborative software development in Python. Skills in data analysis and model evaluation. Experience of performance tuning in ML systems. Experience communicating in English, both written and spoken, including the skill to communicate technical results and justify assumptions to diverse technical audiences. Willingness and flexibility to operate in a highly agile and dynamic environment. Doctorate (PhD) in Computer Science or related area, or equivalent training and experience in research. Demonstrated ability to work in large codebases. Proficient in lower-level engineering skills (eg C/C++/Rust) or equivalent systems languages. Experience of cluster-based distributed data processing techniques.