Job Description

Company
Cynapse Pte Ltd
cynapse.ai
Designation
Computer Vision Model Engineer Intern
Date Listed
16 Dec 2025
Job Type
Entry Level / Junior Executive
Intern/TS
Job Period
From Jan 2026, For At Least 6 Months
Profession
IT / Information Technology
Industry
Artificial Intelligence / Smart Automation
Location Name
71 Ayer Rajah Crescent, #04 21, SingaporeSingapore 139951, Singapore
Address
71 Ayer Rajah Crescent, #04 21, Singapore 139951
Map
Allowance / Remuneration
$1,100 - 1,500 monthly
Company Profile

About Cynapse

Cynapse is a leading AI software company specializing in enterprise-grade Video Intelligence Solutions Powered by Generative AI, tailored to meet the unique challenges of various industries. Our vertical-specific solutions empower organizations to enhance safety, operational efficiency, and security in complex environments such as roads, seaports, airports, and cities. By combining advanced Vision AI with Generative AI, we continually push the boundaries of video analytics, delivering insights and automation that transform operations.


Led by a global team with a proven track record of scaling startups into market leaders, we foster innovation, collaboration, and diverse perspectives. Headquartered from US, Cynapse serves clients worldwide, redefining what's possible with video intelligence.

We are seeking a hands-on

Computer Vision Model Engineering Intern

with a strong interest in training deep learning models, refining architectures, and building real-world computer vision systems.


This role is ideal for builders who enjoy running experiments, analysing model behaviour, and driving measurable performance improvements. You will work closely with experienced model engineers on production-grade computer vision models, ranging from standard detectors to modern zero-shot and open-vocabulary architectures.

What You'll Do

Train & Tune Models

Train, evaluate, and improve deep learning models across a wide range of computer vision tasks, including (but not limited to) classification, object detection, segmentation, and action recognition.

Architecture Exploration

Research and experiment with model architectures (CNNs, Transformers), training strategies, loss functions, data augmentation, and optimization techniques.

Failure

Analysis

Analyse model failures (false positives/negatives, edge cases) and propose practical solutions through data refinement, architectural changes, or improved inference strategies.

Data Engineering & Experimentation

Work with large-scale datasets, including data cleaning, preprocessing, and versioning to support reproducible experiments.

Pipeline & Tooling Support

Contribute to training and evaluation pipelines by improving automation, reliability, and experimentation efficiency.

Who This Role Is For

Someone who enjoy training models, tweaking architectures, and iterating through experiments

.

Problem solvers who like debugging why models fail and understanding root causes. Builders who prefer hands-on engineering over purely theoretical work. Pragmatists excited to work on models that deploy into production environments.

Requirements

Pursuing or completed a Diploma, Bachelor's, or Master's in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, or a related field. Strong interest in deep learning model training and experimentation. Proficiency in Python. Experience with PyTorch or TensorFlow through coursework or projects. Analytical mindset with the ability to reason about model behaviour and failure cases. Comfortable working in a Linux environment (SSH, file management, basic shell scripting).

Bonus Skills

Hands-on experience training computer vision models (e.g., CNNs, Transformers, YOLO). Familiarity with vision-language models (e.g., CLIP, zero-shot or open-vocabulary detection). Familiarity with ML engineering tools and workflows, such as Git, Docker, and experiment tracking systems (e.g., MLflow, Weights & Biases, DVC). Knowledge of computer vision libraries (e.g., OpenCV) and video processing tools (e.g., ffmpeg). Interest in generative AI, multi-modal learning, or real-time video analytics.

Internship Details

Duration

: Minimum 6 months (flexible).

Commitment

: At least 4 days per week (full-time preferred).

Schedule

: Flexible start and end dates to accommodate exams or personal schedules.

Location

: Due to the nature of the role, candidates must be based in Singapore or have relevant experience studying/working in Singapore.



Application Instructions
Submit your text CV and answer the following questions using InternSG:



1. Are you currently residing in Singapore?

2. Are you legally authorized to work in Singapore?

3. Will you now, or in the future, require sponsorship for employment visa status?

4. Can you commit at least 4 days a week for minimally 6 months?



Kindly note that only shortlisted candidates will be notified.