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AI Engineer (Multimodal Systems)

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Onsite

AI Engineer (Multimodal Systems)

Razer Inc.

AI Engineer (Multimodal Systems)

Skills

PyTorchTensorFlowMachine LearningAIWorkday

4 days ago

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Important information


Contract type:

Permanent

Location:

Paris, France

Work mode:

Onsite

Published on:

15 October 2026

What they need


  • Designs and implements AI components for in-game event recognition, multimodal learning, and model optimization
  • Develops CV and audio processing pipelines for automated dataset generation and labeling
  • Collaborates on training workflows for the top 100, 500, and 3000 games
  • Builds the video decluttering pipeline to filter irrelevant gameplay segments
  • Works closely with the Lead Engineer on model deployment optimization and SDK integration
  • Implements and maintains C++ modules for data acquisition, inference, and model serving
  • Focused on performance tuning and inference latency reduction for on-device intelligence
  • Develop and optimize AI components for multimodal in-game event recognition, video dataset generation, and model deployment
  • Build AI modules for CV/audio-based in-game event detection
  • Implement data preprocessing and feature extraction pipelines
  • Train, validate, and deploy models across top 100–3000 games
  • Design video decluttering pipeline for gameplay quality refinement
  • Collaborate with SDK developers for on-device inference optimization
  • Ensure low-latency, high-accuracy inference performance- Systematic approach to scalability, reproducibility, and deployment
  • Multimodal data processing (video, audio, metadata)
  • Rigorous analytical and debugging skills in performance-critical contexts
  • Applied AI engineering and model lifecycle understanding
  • Experience with OpenCV, PyTorch, or TensorFlow for model integration
  • Experience with real-time inference and model compression for deployment
  • Proficient in C++ for high-performance implementation
  • Background in computer vision or audio event classification
  • Familiarity with dataset management, labeling, and augmentation techniques
  • Solid understanding of ML fundamentals (CNNs, transformers, multimodal fusion)

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