Machine Learning Research Engineer

• Master’s degree or Postgraduate (preferred) qualification from a Top 200 Global University, preferably in a technical subject

This is a fantastic opportunity to join market-leading UK AI company, Luminance. Named in Tech Nation’s prestigious Future Fifty list and the recipient of two Queen’s Awards, Luminance is the world’s most advanced AI technology which is disrupting the legal profession.

Luminance is looking for dedicated and enthusiastic individuals with a strong academic background in Artificial Intelligence or a related field to join their R&D team based in Cambridge, UK. Successful candidates will be part of a small, dynamic team responsible for: prototyping, building and implementing new approaches to AI problems; and determining output quality, efficiency, and feasibility compared with other techniques in a data-driven manner.

This role is open to both graduates with a willingness to learn the desired skills, and more senior candidates with proven, prior experience in them.

If you are excited about this role but your skill or experience doesn’t perfectly align with the job description, we encourage you to apply anyway. At Luminance we are dedicated to building and promoting an inclusive workplace where everyone can flourish. You might just be who we’re looking for, for this role or another – enthusiasm is by far the most important requirement for any applicant to Luminance!

Desired Skills

• Strong analytical skills and ability to approach tasks in a scientific manner
• Data science skills, including handling of large data sets, modelling and evaluation
• Good understanding of software engineering concepts, particularly with machine learning development
• Experience with machine learning Python frameworks such as PyTorch, Tensorflow and Scikit-Learn
• For more senior candidates:
• Understanding of, including published work within, related academic fields such as NLP, Computer Vision or Deep Learning
• Relevant industry experience with demonstrable impact in a commercial setting
• Significant practical usage of modern ML areas (NLP, Transformers, LLMs, NER, GANs, Reinforcement Learning, etc.)