Electric Twin is building advanced systems to predict human behaviour. We are backed by Europe's leading seed-stage investors and have assembled an exceptional team. We are hiring an ML engineer to work on improving our ability to predict human behaviour and play a central role in our work with customers.

Responsibilities

• Design and implement state-of-the-art machine learning models to predict human decision making.
• Propose areas of research to improve our ability to predict human behaviour, and the performance of our models and systems.
• Use focus group conversations, surveys and other contextual data in order to fine-tune language models to achieve better representations of target populations.
• Develop evaluation methodologies to de-blackbox LLM decision pathways for the underlying dataset.
• Successfully implement machine learning techniques to interpret and analyse data; iterate on feedback to improve the value of our models in customer use cases.

Requirements

• Experience of implementing and deploying machine learning research ideas within industry.
• Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly.
• Strong statistical modelling experience with examples of using these in real-world applications.
• Proficiency in Python, with a focus on statistical, ML, data analysis and visualisation libraries: pandas, numpy, statsmodels, scikit-learn, pytorch.
• Deep Learning knowledge with examples of completing projects in this area.
• Keen interest in using LLMs.

Desirable

• Not afraid of using and developing unorthodox methods to make progress on difficult problems.
• Product-minded outlook: You can build for customers and iterate on their feedback.
• Startup mindset: You value the importance of moving quickly, getting things done and picking up tasks which are not on your to-do list.

Benefits

• Stock Option Plan
• Pension
• Health Care Plan (Medical, Dental & Vision)
• Life Insurance (Basic, Voluntary & AD&D)
• Paid Time Off (Vacation, Sick & Public Holidays)
• Family Leave (Maternity, Paternity)
• Training & Development