AI Engineer, Echobox

• A degree in Computer Science, Data Science, or a related field (or equivalent practical experience)
• 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch)
• Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems
• Strong Python and Java programming skills and familiarity with NLP algorithms and libraries
• Solid understanding of basic statistics and how to apply it to real-world machine learning tasks
• Familiarity with cloud platforms (AWS) and Kubernetes for deploying and scaling models
• A passion for solving problems with data and machine learning, always looking for ways to get things done quickly and effectively
• A proactive, results-driven mindset—eager to take ownership of tasks and deliver value without waiting for direction
• Ability to work independently, learn fast, and iterate without being bogged down by unnecessary processes
• Fluent written and spoken English

Desirable

• You will report to our Head of Data Science and work closely with our Product managers, Software engineers and Data Scientists to define and execute on the future path for our products
• Work closely with senior engineers and data scientists to quickly learn and apply machine learning techniques to real-world problems, shipping results fast, all whilst meeting launch deadlines
• Take ownership of end-to-end ML model development—from data preprocessing and feature engineering to training, testing, and deployment
• Collaborate across teams to implement machine learning solutions into production systems, ensuring that models are scalable, reliable, and effective
• Actively contribute to refining and improving existing models and systems. If something can be optimized, you're on it—constantly looking for ways to enhance performance
• Quickly analyze data and generate insights to drive product decisions. You’ll focus on making things work fast and efficiently, without over-complicating the process
• Document your work and share findings clearly with the team. No jargon—just straightforward, actionable insights
• Continuously learn new techniques and stay up to date with the latest ML trends, applying them to improve the product as you go