Machine Learning Engineer

Responsibilities

Model Development & Training

• Train and fine-tune computer vision models for use cases such as object detection, classification, segmentation, pose estimation, and video analytics
• Improve model performance through architecture tuning, optimisation strategies, and experimentation
• Work on data augmentation, loss tuning, learning rate scheduling, and training improvements
• Evaluate newer vision and multimodal AI approaches where applicable

Research & Experimentation

• Study and apply insights from technical papers and industry research
• Prototype and benchmark different modelling approaches against baseline systems
• Analyse experiment outcomes and document findings, limitations, and trade-offs

Model Evaluation & Debugging

• Investigate edge cases, prediction inconsistencies, and real-world model behaviour
• Troubleshoot issues such as false positives/negatives and unstable inference results
• Improve model robustness through better datasets, inference logic, and tuning strategies

Data & ML Infrastructure

• Work with large-scale datasets including preprocessing, validation, and quality checks
• Support experiment tracking, reproducibility, and dataset versioning workflows
• Develop maintainable experiment pipelines and configuration-based training setups

Deployment & MLOps

• Assist with deployment and monitoring of ML models in production
• Work with training, inference, and evaluation pipelines
• Support model lifecycle processes including retraining, validation, rollback, and version control
• Consider optimisation factors such as latency, GPU utilisation, and inference efficiency

Requirements

• Degree in Computer Science, AI, Machine Learning, or related field (or equivalent practical experience)
• Around 1–2+ years of hands-on experience in deep learning or computer vision projects
• Strong Python programming skills
• Experience with PyTorch and/or TensorFlow
• Familiarity with Linux environments and command-line usage

Nice-to-Have Skills

• Exposure to multimodal or open-vocabulary AI models
• Experience customising model architectures, layers, or loss functions
• Familiarity with tools such as Git, Docker, MLflow, or Weights & Biases
• Knowledge of ONNX, TensorRT, quantisation, or model optimisation techniques
• Experience handling video data using OpenCV or FFmpeg