Senior Computer Vision Scientist - Real-Time AI for Gaming

Razer Inc.

Singapore

On-site

SGD 90,000 - 150,000

Full time

9 days ago
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Job summary

Razer Inc. is seeking a skilled Computer Vision Engineer in Singapore to design and implement advanced CV solutions for interactive systems.

You will work closely with data engineers, game developers, and software engineers to build scalable, production-ready vision systems enabling robust perception and fast iteration in real-world environments. The ideal candidate brings strong experience in object detection, tracking, segmentation, image classification, and video analysis, with familiarity in

Qualifications

  • Experience developing CV algorithms for real-world apps.
  • Proficiency in Python or C++.
  • Strong image/video processing knowledge and methods.
  • Hands-on with OpenCV and deep learning frameworks (PyTorch, TensorFlow).
  • Experience with annotated datasets for CV model training.
  • Ability to evaluate and optimize model performance using metrics.

Responsibilities

  • Design and implement CV algorithms for object detection, tracking, segmentation, and video understanding.
  • Build end-to-end CV systems meeting performance and scalability needs.
  • Collaborate with data engineers to design labeling schemas and data validation for large datasets.
  • Train, fine-tune, and optimize CV models using ML frameworks.
  • Evaluate model performance with appropriate metrics and benchmarks.
  • Optimize models for real-time inference and edge deployment.

Skills

Python
C++
OpenCV
PyTorch
TensorFlow
Data labeling
Model evaluation
Multimodal AI

Education

Master's degree
PhD

Tools

RabbitMQ
gRPC
REST APIs

Job description

Razer Inc. is seeking a skilled Computer Vision Engineer in Singapore to design and implement advanced CV solutions for interactive systems.

You will work closely with data engineers, game developers, and software engineers to build scalable, production-ready vision systems enabling robust perception and fast iteration in real-world environments. The ideal candidate brings strong experience in object detection, tracking, segmentation, image classification, and video analysis, with familiarity in

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