Machine Learning Engineer Intern (Computer Vision & AI)

InternSG; Singapore; On-site; SGD 13,000 - 20,000; Part time

Job summary

InternSG is seeking a hands-on Machine Learning Engineer Intern to join our Model Engineering Team in Singapore, focusing on Computer Vision and modern AI. You will train deep learning models, refine architectures, and scale production-ready vision systems alongside experienced engineers.

This internship offers exposure to training, evaluation, and deployment pipelines, with opportunities to work on open-vocabulary detection, zero-shot methods, and data-centric improvements that drive measurable

Qualifications

• Pursuing or completed degree in Computer Science, AI, ML, or related field.
• Strong interest in deep learning model training, experimentation, and data-centric AI.
• Proficiency in Python and experience with PyTorch or TensorFlow.
• Competency in a Linux environment (SSH, shell scripting, and CLI-based file management).

Responsibilities

• Train, evaluate, and improve deep learning models across CV tasks such as classification, object detection, and segmentation.
• Research and experiment with CNNs, Transformers, and Foundation Models for open-vocabulary detection.
• Debug model failures and implement data annotation, cleaning, or improved inference strategies.
• Manage large-scale datasets, including preprocessing and versioning for reproducible experiments.
• Contribute to automated training and evaluation pipelines to improve reliability and deployment efficiency.

Skills

Python

Linux

SSH

Shell scripting

Education

Pursuing or completed degree in Computer Science/AI/ML or related

Tools

OpenCV

FFmpeg

PyTorch

TensorFlow

Git

Docker

Weights & Biases

MLflow

Job description

The Role

We are seeking a hands‑on Machine Learning Engineer Intern to join our Model Engineering Team with a focus on Computer Vision and modern AI. This role is designed for builders who are passionate about training deep learning models, refining architectures, and scaling real-world vision systems.

You will work closely with experienced engineers on production‑grade models, ranging from standard object detectors to cutting‑edge zero‑shot and open‑vocabulary architectures. This is an ideal role for those who enjoy running experiments, analysing model behaviour, and driving measurable performance improvements.

What You’ll Do

• Model Training & Fine-Tuning: Train, evaluate, and improve deep learning models across a variety of tasks, including classification, object detection, segmentation, and action recognition.
• Architecture & AI Exploration: Research and experiment with CNNs, Transformers, and Foundation Models. You will explore the integration of modern AI to enhance open‑vocabulary detection and zero‑shot capabilities.
• Failure Analysis & Data Refinement: Debug model failures (false positives/negatives) and implement solutions through targeted data annotation, cleaning, or improved inference strategies to ensure model robustness.
• Data & ML Engineering: Manage large-scale datasets, including preprocessing and versioning, to ensure high‑fidelity and reproducible experiments.
• Pipeline Automation: Contribute to the development and optimization of automated training and evaluation pipelines to improve reliability and deployment efficiency.

Who This Role Is For

• Builders: You prefer hands‑on engineering and practical implementation over purely theoretical research.
• Experimenters: You enjoy the iterative process of testing different architectures, hyperparameters, and datasets to maximize performance.
• Problem Solvers: You are curious about root causes and enjoy the "detective work" required for model debugging and data‑driven improvements.
• Pragmatists: You are excited to see your models and automated systems successfully deployed in real‑world production environments

Internship Intake Periods

We are currently seeking interns for:

• October 2026 – April 2027
• December 2026 – June 2027

Requirements

• Pursuing or completed a degree in Computer Science, AI, Machine Learning, or a related technical field.
• Strong interest in deep learning model training, experimentation, and data‑centric AI.
• Proficiency in Python and experience with PyTorch or TensorFlow.
• Competency in a Linux environment (SSH, shell scripting, and CLI-based file management)

Bonus Points

• Hands‑on Experience: Previous work with specialized CV frameworks or comparing multiple architectures.
• Modern AI Knowledge: Familiarity with vision‑language models or zero‑shot detection.
• MLOps Tools: Experience with Git, Docker, or experiment tracking tools (e.g., Weights & Biases, MLflow).
• Video Processing: Knowledge of OpenCV, FFmpeg, or handling video streams.

Internship Details

1. Duration: Minimum 6 months
2. Commitment: 4–5 days per week (Preferably 5 day)
3. Location: Singapore-based

Note: Due to the nature of the role, the position is considered only for candidates who are based in Singapore or have experience studying and/or working in Singapore