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About This Role
- About the team: Network and Collectives owns the scale-out communication substrate for our AI accelerator - the NCCL equivalent for our hardware.
- We turn many chips into one machine: all-reduce, all-gather, reduce-scatter and point-to-point, made topology-aware and mapped onto the rack/pod interconnect.
- We are measured on real fabric across a pod, not on a single card, and we pair tightly with the Tray/Rack/Pod/Cluster HW pillar and the Multi-Node Runtime team.
- What you will do: Design and implement collective algorithms tuned to our interconnect topology and bandwidth/latency profile.
- Build a topology-aware transport layer over the tray/rack/pod.
- Optimize end-to-end collective performance across multi-node pods; profile, find bottlenecks, and close the gap to fabric peak.
- Co-design with HW on interconnect features, and with Multi-Node Runtime on partitioning, overlap, and scheduling.
- Own correctness and numerical determinism of reductions across scale-out.
Qualifications
- 5+ years AI/Systems/HPC software in C++; strong concurrency and lock-free design.
- Hands-on with collective libraries (NCCL/MPI/etc) or scale-out communication.
- Working knowledge of the communication patterns behind tensor, pipeline, and expert parallelism, and how they map onto collectives and the underlying fabric.
- Performance engineering: profiling, bandwidth/latency tuning, roofline reasoning.
- Nice-to-have Topology/placement algorithms; congestion control; multi-tenant fabrics.
- Large-scale distributed training/inference exposure.
- Experience with large-scale inference serving stacks (vLLM, SGLang, TensorRT-LLM, DeepSeek).
- RDMA/InfiniBand/RoCE, GPUDirect-style transfers, or comparable fabric experience.
Tools & Skills
Languages
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Job ID
/job/Israel-Haifa/Senior-Software-Developer---Network-and-Collectives_JR0285884
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