Inference
Genesis· Engineering & Research
Hybrid (Paris), Hybrid (London)Full-timePosted 5mo ago
About the role
Build low-latency inference pipelines for on-device deployment, design distributed GPU-accelerated inference systems, and develop low-level performance optimizations and tooling to ensure reliable, real-time AI inference for robotics.
Responsibilities
- 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
- 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
Qualifications
- 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)
- 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
Skills
Required:Distributed systemsPythonC++, Rust, or GoCUDATritonQuantization
Last updated Jul 16, 2026. Interested? Apply on the company site.
About Genesis
A new future is at hand.
We are a new generation of robotics builders, united by a shared mission to push the boundaries of physical AI.
AIGENEGenesisfrontier-labsheadline-ai-europe-100-2026intelligencerobotics
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