Software Engineer, Workload Enablement

Key Responsibilities

• 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, frontend, scale-up, and scale-out networking (including WAN traffic, NVlink and RDMA collectives), storage, thermals, and any other relevant parts.
• 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
• Create repeatable test harnesses that run in CI / lab environments and produce actionable outputs (pass/fail, performance score, regression detection).
• Partner with systems + fleet bring-up engineers to ensure the platform is not only stable and performant, but also operationally usable and scalable (containerization, K8s integration, telemetry hooks, failure triage loops).
• Work cross-functionally with vendors and internal stakeholders by producing clear bug reports, minimal repros, and prioritized issue lists.

Qualifications

• 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)
  • Experience with RDMA and debugging/optimizing comms libraries (NCCL or RCCL) and their interaction with hardware/network
• Proficiency in Python plus comfort reading/writing performance-critical code (C++/CUDA/HIP is a plus).
• Strong profiling/debugging skills (e.g., Nsight, rocprof, perf, flamegraphs; ability to reason from traces/counters).

Preferred Skills

• Experience building workload-shaped benchmarks and stress/fault tests that correlate to production behavior (not just synthetic loops or microbenchmarks).
• Familiarity with RDMA networking and transport tuning; understanding of how network topology and congestion impact collectives.
• Experience running and validating workloads in Kubernetes, and bridging “research code” into robust, repeatable infrastructure.
• Hands-on lab experience with early hardware (new NICs, new GPUs/accelerators, early racks).