Senior Software Engineer II

il y a 4 jours

Lille, Hauts-de-France Razer Inc. Temps plein

Joining Razer will place you on a global mission to revolutionize the way the world games. Razer is
a place to do great work
, offering you the opportunity to make an impact globally while working across a global team located across 5 continents. Razer is also
a great place to work,
providing you the unique, gamer-centric experience that will put you in an accelerated growth, both personally and professionally.

Job Responsibilities
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.

Mission
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.

Competencies

  • Applied AI engineering and model lifecycle understanding.
  • Multimodal data processing (video, audio, metadata).
  • Rigorous analytical and debugging skills in performance-critical contexts.
  • Systematic approach to scalability, reproducibility, and deployment.

Pre-Requisites

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

Are you game?

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