ML Research Engineer

EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

EVOLUTION RECRUITMENT SOLUTIONS PTE. LTD. is seeking an Applied AI Researcher / ML Research Engineer to join the AI Product team. The role focuses on deploying AI models into real-world products with measurable business impact.

The candidate should have hands-on experience with CNNs and Transformer-based models, and be able to translate research into production-ready solutions while collaborating with product, engineering, and business teams.

Qualifications

• Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, AI, ML, Data Science, or related field; Master’s or PhD preferred.
• Hands-on experience with CNNs and Transformer-based models.
• Experience training, fine-tuning, evaluating, or improving deep learning models.
• Strong understanding of deep learning fundamentals, architectures, loss functions, optimization, and training dynamics.

Responsibilities

• Design, train, fine-tune, evaluate, and improve deep learning models for real-world AI applications.
• Work with neural network architectures, including CNNs and Transformer-based models.
• Conduct experiments to validate model performance, reliability, and generalisation on unseen data.
• Select and apply loss functions, architectures, and training strategies based on the problem.

Skills

CNNs

Transformer models

Python

Model evaluation

Education

Bachelor's degree

Master's degree or PhD preferred

Tools

PyTorch

TensorFlow

Job description

We are looking for an Applied AI Researcher / ML Research Engineer to join the AI Product team. This is a deployment-focused role, where the work goes beyond research, experimentation, or proof-of-concept development. The successful candidate will work on AI models and solutions that are expected to be deployed into real-world products. This role is suitable for someone with strong hands-on experience in neural network architectures, especially CNNs and Transformer-based models, and who enjoys solving practical AI problems with measurable business impact.

Responsibilities

• Design, train, fine-tune, evaluate, and improve deep learning models for real-world AI applications.

• Work with neural network architectures, including CNNs and Transformer-based models.

• Conduct experiments to validate model performance, reliability, and generalisation on unseen data.

• Select and apply appropriate loss functions, model architectures, and training strategies based on the problem being solved.

• Analyse model performance, identify weaknesses, and improve accuracy, robustness, and efficiency.

• Translate applied research into deployable AI solutions, not just prototypes or proof-of-concepts.

• Collaborate with product, engineering, and business teams to ensure AI solutions are practical, scalable, and production-ready.

• Deliver research and model development work with a strong focus on measurable real-world impact.

Requirements

• Bachelor’s degree or higher in Computer Science, Engineering, Mathematics, Artificial Intelligence, Machine Learning, Data Science, or a related field. Master’s degree or PHD preferred.

• Hands-on experience with neural network architectures, especially CNNs and/or Transformer-based models.

• Experience training, fine-tuning, evaluating, or improving deep learning models.

• Strong understanding of deep learning fundamentals, including model architectures, loss functions, optimisation methods, and training dynamics.

• Ability to evaluate how well a model generalises to unseen data.

• Hands-on experience with PyTorch and/or TensorFlow.

• Strong programming skills, especially in Python.

• Ability to explain technical model decisions clearly, including architecture choice, loss function selection, and performance trade-offs.

• Practical mindset with the ability to move applied research into deployment-ready solutions.

Preferred Qualifications

• Experience working on AI/ML solutions that have been deployed or prepared for production use.

• Experience with computer vision, NLP, large language models, vision-language models, or foundation models.

• Experience with model evaluation, benchmarking, performance optimisation, and experiment tracking.

• Familiarity with real-world deployment considerations such as reliability, scalability, latency, and efficiency.