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GE
Genesis
Inference
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Location
Paris
Work mode
hybrid
Type
full-time
Department
Engineering & Research
Last seen
23h ago
What You’ll Do
•
Build low-latency inference pipelines for on-device deployment, enabling real-time next-token and diffusion-based control loops in robotics
•
Design and optimize distributed inference systems on GPU clusters, pushing throughput with large-batch serving and efficient resource utilization
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Implement efficient low-level code (CUDA, Triton, custom kernels) and integrate it seamlessly into high-level frameworks
•
Optimize workloads for both throughput (batching, scheduling, quantization) and latency (caching, memory management, graph compilation)
•
Develop monitoring and debugging tools to guarantee reliability, determinism, and rapid diagnosis of regressions across both stacks
What You’ll Bring
•
Deep experience in distributed systems, ML infrastructure, or high-performance serving (8+ years)
•
Production-grade expertise in Python, with strong background in systems languages (C++/Rust/Go)
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Low-level performance mastery: CUDA, Triton, kernel optimization, quantization, memory and compute scheduling
•
Proven track record scaling inference workloads in both throughput-oriented cluster environments and latency-critical on-device deployments
•
System-level mindset with a history of tuning hardware–software interactions for maximum efficiency, throughput, and responsiveness
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