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Razer

Senior AI Engineer

Salary€44k – €82k
LocationFrance; Singapore
Typefull-time
SenioritySenior
Experience3+ yrs
Company size10,000+ people
First seenOct 6, 2026 · 5d ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Must have6
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
Skills
C++
PyTorch
ONNX Runtime
TensorRT
llama.cpp
GGML
About the company
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.
Job Responsibilities :
About the role
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.
Pre-Requisites :
•
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.
Salary Ranges:
€44,356.00 - €82,020.00 (per annum)
Legal
Disclaimer: Exact compensation may vary based on skills, experience, and location.
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.
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