Machine Learning Research Engineer

Evolution Recruitment Solutions Pte Ltd; Singapore; On-site; SGD 90,000 - 150,000; Full time

Job summary

Evolution Recruitment Solutions Pte Ltd is seeking an ML Research Engineer in Singapore to join an AI Product team. You will work hands-on with neural networks, including CNNs and Transformer-based models, training and refining deep learning systems for real-world applications.

You will design experiments, evaluate generalisation, and contribute to production-ready AI solutions alongside product and engineering teams.

Qualifications

• Master's or PhD in Computer Science, AI, ML, Mathematics or related field.
• Hands-on experience with neural network architectures (CNNs/Transformers).
• Experience training, fine-tuning, evaluating or improving deep learning models.

Responsibilities

• Designing and experimenting with neural network architectures (CNNs/Transformers).
• Training, fine-tuning and improving deep learning models for practical AI applications.
• Conducting structured experiments to evaluate model performance and reliability.
• Collaborating with product and engineering teams to translate research into production solutions.
• Exploring Transformer-based, LLM and/or Vision-Language Model technologies.

Skills

CNNs

Transformer models

Python

Deep learning

Model training

Loss functions

Generalisation

Education

Master's or PhD in CS/AI/ML

Tools

PyTorch

TensorFlow

Job description

Our client is an AI technology company developing advanced solutions for enterprise and real-world applications. They are looking for a ML Research Engineer to join their AI Product team in Singapore.

This is a hands-on research and engineering opportunity for someone who wants to work deeply with machine learning and deep learning models, rather than simply consuming pre-trained models or APIs.

You will be involved in

• Designing and experimenting with neural network architectures, particularly CNNs and/or Transformer-based models
• Training, fine-tuning and improving deep learning models for practical AI applications
• Conducting structured experiments to evaluate model performance and reliability
• Working with different training strategies, architectures and optimisation approaches
• Selecting appropriate loss functions and understanding their impact on model performance
• Evaluating how models generalise to unseen data and investigating potential limitations
• Analysing experimental results and identifying opportunities to improve accuracy, robustness and efficiency
• Working with datasets and data preparation as part of the model development process
• Developing prototypes and proof-of-concept solutions to validate new AI approaches
• Exploring modern AI developments, including Transformer-based, LLM and/or Vision-Language Model technologies
• Collaborating with product and engineering teams to translate research ideas into practical, production-oriented solutions
• The role provides exposure to the full ML research and development lifecycle, from architecture selection and experimentation through to model evaluation and optimisation.

Requirements

• Master's or PhD in Computer Science, AI, Machine Learning, Mathematics, Engineering or a related technical field
• Hands-on experience with neural network architectures, particularly CNNs and/or Transformer-based models
• Demonstrated experience training, fine-tuning, evaluating or improving deep learning models, rather than only consuming pre-trained models or AI APIs
• Experience evaluating model generalisation on unseen data
• Understanding of loss functions and the ability to explain why a particular loss function was selectedAbility to explain and justify the model architecture used in previous AI/ML projects
• Strong foundation in deep learning concepts, model training and optimisation
• Strong Python/programming skills
• Genuine interest in AI/ML research and deep learning

Nice to Have:

• Experience with PyTorch, TensorFlow or similar frameworks
• Experience with LLMs, VLMs or foundation models
• Experience with transfer learning, fine-tuning or knowledge distillation
• Research publications or academic projects in AI/ML
• Exposure to areas such as computer vision, NLP, reinforcement learning, edge AI, robotics, speech or time-series modelling

Please note that visa sponsorship is not available at this time.