On-Device AI Engineer for Real-Time Gaming

Razer Inc.; France; On-site; EUR 75,000 - 110,000; Full time

Job summary

Jobtailor is seeking an AI/ML engineer to design and ship on-device models for gaming, biosensing, and peripherals. You will implement production-grade inference with tight latency and resource budgets, and write efficient C++ for runtime and SDK layers.

Work across platforms with ML frameworks like PyTorch and TensorRT, optimize models via quantization/pruning, and collaborate with platform, haptics, and audio teams to expose AI capabilities to game studios.

Qualifications

• 3+ years in AI/ML engineering or related roles.
• Proficient in C++ with real-time or systems programming.
• Experience deploying ML models on-device/edge.
• Familiarity with ML frameworks and runtimes (e.g. PyTorch, ONNX Runtime, TensorRT).
• Understanding model optimization techniques (quantization, pruning, distillation) and the trade-offs.

Responsibilities

• Design and ship local on-device AI models for gaming, biosensing, and peripherals.
• Bridge ML and real-time systems to production-grade inference with tight latency budgets.
• Implement and optimize AI/ML models for on-device inference in latency-sensitive contexts.
• Process biosignal and sensor data in real time.
• Optimize models for performance and footprint (quantization, pruning, acceleration).
• Write efficient production-quality C++ for runtime and inference layers of the SDK.
• Collaborate with platform, haptics, and audio teams to expose AI capabilities via APIs.
• Profile, benchmark, and improve inference speed, memory use, and energy efficiency.

Skills

C++ proficiency

Real-time/systems

Edge AI deployment

Tools

PyTorch

ONNX Runtime

TensorRT

llama.cpp / GGML

Job description

Jobtailor is seeking an AI/ML engineer to design and ship on-device models for gaming, biosensing, and peripherals. You will implement production-grade inference with tight latency and resource budgets, and write efficient C++ for runtime and SDK layers.

Work across platforms with ML frameworks like PyTorch and TensorRT, optimize models via quantization/pruning, and collaborate with platform, haptics, and audio teams to expose AI capabilities to game studios.