Jr. Machine Learning Engineer

Company Description Digital Roundabout Pte Ltd is dedicated to continuous innovation, treating it as an ongoing process rather than a one-time event. The company operates across multiple sectors, including content localization, IT service management (ITSM), broadcast media localization, audience measurement, and e-notes. Digital Roundabout focuses on developing and deploying AI models that deliver complete end-to-end solutions for specific market segments.

Team members join a dynamic environment where adaptability, experimentation, and practical application of AI technologies are highly valued.

Role Description

• Research and identify suitable existing ML/DL models for specific business requirements.
• Adapt, fine-tune, and customise pretrained models for our use cases.
• Experiment with different models and techniques to improve accuracy and performance.
• Work on computer vision, image processing, video analytics, audio processing, speech recognition, and generative AI applications.
• Prepare datasets, perform preprocessing, annotation, and augmentation for image, video, and audio data.
• Evaluate model performance and optimise inference speed and resource utilisation.
• Implement research papers and explore new open-source AI/ML technologies.
• Collaborate with the engineering team to integrate and deploy developed models.

This is a full-time remote role for a Jr. Machine Learning Engineer. The role involves assisting in the design, training, and evaluation of machine learning models, with a focus on pattern recognition and neural networks for content localization and related applications.

Daily tasks include data preprocessing, feature engineering, implementing algorithms, running experiments, and documenting results. The Jr. Machine Learning Engineer will collaborate with senior engineers and product teams to translate business requirements into technical solutions, contribute to model deployment workflows, and help maintain existing models and pipelines.

The position also includes learning and applying best practices for code quality, version control, and reproducible research in a distributed team environment.

Qualifications

Required Skills

• Bachelor's degree in Computer Science, AI/ML, or a related field.
• Good programming skills in Python/Node/Typescript.
• Understanding of machine learning and deep learning concepts.
• Hands-on knowledge of PyTorch or TensorFlow.
• Familiarity with OpenCV, NumPy, Pandas, and Scikit-learn.
• Understanding of model fine-tuning, evaluation, and inference.

Good to Have

• Experience with Hugging Face and open-source ML models.
• Knowledge of CNNs, Transformers, and computer vision.
• Exposure to audio processing, speech-to-text (STT), text-to-speech (TTS), and speaker diarization.
• Knowledge of GPU computing, CUDA, and model optimisation.
• Experience with image, video, and audio processing or generative AI.
• Strong foundation in Computer Science and Algorithms, with the ability to implement and optimize model training and inference workflows.
• Knowledge of Statistics and Pattern Recognition to analyze data, evaluate models, and interpret performance metrics.
• Understanding of Neural Networks and modern machine learning techniques, including experience with at least one common ML framework (e.g., TensorFlow, PyTorch, scikit-learn).
• Bachelor’s degree or equivalent practical experience in Computer Science, Data Science, Engineering, Mathematics, or a related field.
• Proficiency in programming languages commonly used in ML (such as Python), and familiarity with version control tools (e.g., Git).
• Ability to work independently in a remote setting, collaborate effectively with a diverse team, and communicate technical concepts clearly.
• Experience or interest in applications such as content localization, media technologies, or ITSM is an advantage.
• Willingness to learn new tools and methods, and to contribute to a culture of continuous improvement and innovation.