Software Engineer, Workload Enablement

Job summary

Responsibilities

We’re hiring an SW Engineer to enable production workloads and end-to-end testing on new platforms. This role will include creating new test harnesses and platform stress benchmarks, porting existing inference and training workloads to new, sometimes early-access, systems/hardware, analyzing…

• Port and validate key inference and training workloads on new platforms/SKUs as they arrive; drive correctness, performance, and stability to an internal readiness bar.
• Build a suite of benchmarks and stress tests that capture real E2E behavior of our workloads by exercising all aspects of a system, including CPU, GPU, memory subsystem…
• Deep-dive performance on distributed training/inference:
• Collective performance and tuning (across NCCL/RCCL and internal libraries)
• Overlap of compute/communication, kernel-level bottlenecks, memory bandwidth and scheduling effects

What they're looking for

• BS in CS/EE (or equivalent practical experience).
• 5+ years in one or more of: ML systems, performance engineering, distributed systems, or HPC.
• Strong hands-on experience with:
• PyTorch and modern LLM training/inference stacks
• Large-scale distributed training concepts (data/model/pipeline parallel, collective comms)

Tech stack

• Kubernetes
• PyTorch
• Python
• C++
• CUDA