Staff Machine Learning Engineer - Applied ML & Research

Identify high-impact ML opportunities and influence stakeholders to prioritise and support these initiatives

Design and develop scalable machine learning models — including classifiers, regressors, and rule-based systems — to solve real-world problems

Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment

Translate complex technical concepts into clear insights for both technical and non-technical stakeholders

Set and guide technical direction across ML projects, ensuring alignment with technical best practices and business goals

Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement

Master's degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field

7+ years of industry experience building and deploying ML models at scale

Proven ability to lead cross-functional technical initiatives and influence engineering strategy

Proficiency in Python (with libraries such as PyTorch, XGBoost, and Scikit-learn) and SQL

Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar

Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies

A track record of shipping production-level ML products and maintaining high code quality

Excellent problem-solving skills and the ability to scope and disambiguate complex ML projects into clear, achievable milestones