Evolution Singapore Hiring! Full Time AI Research Engineer in - Ricebowl

Applied AI Researcher / ML Research Engineer

Role Overview

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

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 suitable loss functions, model architectures, and training strategies based on the problem being solved.
• Analyse model performance, identify limitations, and improve accuracy, robustness, and efficiency.
• Translate applied research into deployment-ready AI solutions.
• Collaborate with product, engineering, and business teams to ensure AI solutions are practical, scalable, and production-focused.
• Deliver 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 / 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 models generalise 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.