AI Engineer, Multimodal Systems

Francemid

Machine Learning EngineerData

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Quick Summary

Key Responsibilities

Designs and implements AI components for in-game event recognition, multimodal learning, and model optimization.

Technical Tools

Machine Learning EngineerData

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 #LifeAtRazer experience that will put you in an accelerated growth, both personally and professionally.

Responsibilities

~1 min read

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.

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.

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.

Razer is proud to be an Equal Opportunity Employer. We believe that diverse teams drive better ideas, better products, and a stronger culture. We are committed to providing an inclusive, respectful, and fair workplace for every employee across all the countries we operate in. We do not discriminate on the basis of race, ethnicity, colour, nationality, ancestry, religion, age, sex, sexual orientation, gender identity or expression, disability, marital status, or any other characteristic protected under local laws. Where needed, we provide reasonable accommodations - including for disability or religious practices - to ensure every team member can perform and contribute at their best.