Machine Learning Specialist

What you will do

• Review, understand and contribute to the ongoing development of the Company's machine-learning engine
• Design, implement and integrate new features, models and technical improvements into the existing infrastructure
• Test, validate and optimise machine-learning models, including hyperparameter optimisation and systematic assessment of performance, robustness and generalisability across different datasets, experimental conditions and input configurations
• Contribute to the development of reliable, scalable and well-documented machine-learning workflows
• Curate, preprocess and perform quality-control checks on datasets when required
• Collaborate closely with the technical team to identify practical solutions to complex data and modelling challenges

Required

• Bachelor's degree in Computer Science, Artificial Intelligence, Machine Learning, Software Engineering, Data Science, Bioinformatics or a closely related field
• At least 10 years of proven professional experience in machine-learning, artificial-intelligence or data-intensive software-development roles
• Advanced proficiency in Python for machine-learning development
• Strong, practical understanding of machine-learning methodologies, model evaluation and optimisation
• Hands-on experience with PyTorch
• Experience developing or training CUDA-accelerated machine-learning workloads
• Familiarity with remote cluster-computing environments, including access and job management through SSH or similar tools
• Demonstrated ability to work with complex datasets, build robust data-preprocessing pipelines and identify data-quality issues
• Strong problem-solving skills, intellectual curiosity and the ability to work independently in a fully remote environment
• Good command of written and spoken English

Preferred

• Experience with transformer-based architectures
• Familiarity with Positive-Unlabeled Learning (PU Learning) or other learning paradigms suited to incomplete, noisy or partially labelled datasets
• Experience with model deployment, MLOps, distributed training or scalable data-processing workflows
• Familiarity with software-engineering best practices, including version control, testing and technical documentation
• Experience working in a fast-paced research, technology or startup environment