Senior AI Engineer

WYVRN SAS
Lille, France
about 2 months ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours
Job source

Tech stack

Clean Code Principles Application Programming Interfaces (APIs) Artificial Intelligence C++ (Programming Language) Profiling Machine Learning Tensorflow Real Time Systems Pytorch ONNX (Open Neural Network Exchange) Format TensorRT

Job description

We’re looking for a Senior AI Engineer to join Razer Technology Team to design and ship local (on-device) AI models that run efficiently across gaming, biosensing, and peripheral applications. You’ll work at the intersection of machine learning and real-time systems - taking models from prototype to optimized, production-grade inference that runs on the player’s machine and our hardware, with tight latency and resource budgets. You’ll be part of a ~25-person R&D team and collaborate closely with our haptics, audio, and platform groups.

  • Implement and optimize AI/ML models for on-device inference in latency-sensitive gaming and peripheral contexts.
  • Build and integrate models that process biosignal and sensor data (e.g. from peripherals and wearables) in real time.
  • Optimize models for performance and footprint - quantization, pruning, and acceleration across CPU/GPU/NPU targets.
  • Write efficient, production-quality C++ for the runtime and inference layers of our SDK.
  • Collaborate with platform, haptics, and audio teams to expose AI capabilities to game studios through clean, well-documented APIs.
  • Profile, benchmark, and continuously improve inference speed, memory use, and energy efficiency.

Requirements

  • 3+ years of experience in AI/ML engineering, applied ML, or a closely related role.
  • Proficiency in C++ (required) - comfortable writing performant, maintainable code in a real-time or systems context.
  • Hands-on experience deploying machine learning models, ideally on-device / edge rather than purely cloud.
  • Familiarity with ML frameworks and runtimes (e.g. PyTorch, ONNX Runtime, TensorRT, llama.cpp / GGML, or similar).
  • Understanding of model optimization techniques (quantization, pruning, distillation) and the trade-offs they involve.
  • Strong fundamentals in performance profiling and working within constrained compute/latency budgets.

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