ABOUT MATIFIC
Matific is a leading global EdTech provider, delivering an adaptive online learning platform for primary school mathematics. With our product being utilised by millions of students, teachers and parents in 100+ countries we are helping educate the youth and bring equality to education. With over $50M USD invested and a global team of over 200+ employees, we are committed to achieving our goals. We’ve also picked up a number of awards including numerous CODiEs, Academics’ Choice and Edtech Digest to name a few.
THE ROLE
We are seeking a skilled Machine Learning Engineer to join our team. This role involves end-to-end ownership of the machine learning lifecycle — from model design and development to scalable deployment and monitoring in production. You will play a key role in developing high-impact ML solutions and ensuring their reliability and performance on our platform. This role combines deep ML research with strong engineering and MLOps practices to bring state-of-the-art models from concept to production.
KEY RESPONSIBILITIES
- Design, develop, and train robust machine learning models, with a focus on transformer-based architectures, large language models (LLMs), and multimodal systems.
- Conduct applied research and experimentation in cutting-edge areas such as agentic AI, few-shot learning, tool-use capabilities and prompt engineering.
- Conduct data exploration, feature engineering, and experimentation to optimize model performance, leveraging SQL for large-scale data analysis and extraction.
- Lead cloud-based deployment and operationalization of ML models, primarily on AWS.
- Build and maintain automated CI/CD pipelines tailored for ML workflows, including data validation, model versioning, testing, and rollout.
- Monitor model performance in production, implement retraining strategies, and manage model drift.
- Scale ML pipelines and inference systems to handle large datasets and high-throughput environments efficiently.
- Collaborate closely with data scientists, software engineers, and product teams to integrate ML solutions into customer-facing products and internal systems.
- Drive best practices in ML system design, model reproducibility, and responsible AI.
- Stay current with emerging trends, tools, and techniques in machine learning, deep learning, and MLOps.
- Bachelor's degree in computer science, Data Science, or a related field. Advanced degrees preferred.
- Minimum of 2 years of hands-on experience in machine learning, with a demonstrable portfolio of projects.
- Deep Learning: Familiarity with neural network architectures, including CNNs, RNNs, and Transformers.
- Proficiency in SQL and data analytics—able to query, transform, and analyze large datasets to support modelling and decision-making.
- Experience working with platforms such as Kubeflow in managing ML experiments is an added benefit.
- Strong skills in evaluating, fine-tuning, and scaling ML models in real-world production settings.
- Proficient in ML frameworks and languages such as TensorFlow, PyTorch, and Python.
- Exceptional problem-solving skills, analytical mindset, and attention to detail.
- Excellent communication skills, both verbal and written.
