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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