AI Engineer (Multimodal Systems)

Razer; Paris; On-site; EUR 70,000 - 110,000; Full time

Job summary

Razer is seeking an experienced AI Engineer to design and optimize multimodal AI components for in-game event recognition and video dataset generation.

The role focuses on on-device inference, C++ implementation, and performance tuning across top games, with collaboration alongside SDK developers and data pipelines.

Qualifications

• Strong background in computer vision or audio event classification.
• Experience with real-time inference and model compression for deployment.
• Proficient in C++ for high-performance implementation.
• Experience with OpenCV, PyTorch, or TensorFlow for model integration.
• Familiarity with dataset management, labeling, and augmentation techniques.
• Solid understanding of ML fundamentals (CNNs, transformers, multimodal fusion).

Responsibilities

• Designs and implements AI components for in-game event recognition and multimodal learning.
• Develops CV and audio processing pipelines for automated dataset generation and labeling.
• Collaborates on training workflows for games across releases.
• Collaborates with SDK developers for on-device inference optimization.
• Implements and maintains C++ modules for data acquisition, inference, and model serving.
• Focuses on performance tuning and 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.

Skills

C++

ML frameworks

Real-time inference

CV/Audio classification

Multimodal data

Model deployment

Performance debugging

Data labeling

ML fundamentals

On-device inference

Tools

OpenCV

PyTorch

TensorFlow

Job description

• 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)