We are looking for early-career Software Engineers to join our team. This is a specialized role sitting at the intersection of high-performance computing (HPC) and Large Language Model (LLM) engineering. You will be responsible for building the automated "speedometer and diagnostic" suite for our next-generation AI infrastructure
In this role, you won’t just be using models; you will be tearing them apart to see how they run on the metal. You will build tools that measure GPU FLOPS, stress-test InfiniBand clusters, and define the benchmarks that ensure our systems are production-ready
Performance Benchmarking: Run and automate standard LLM quality benchmarks (GSM8K, MMLU) alongside custom performance suites for specific workloads (e.g., long-context window, KV cache reuse)
Infrastructure Validation: Create automated acceptance tests for new GPU clusters across x86 and ARM systems, measuring GPU memory bandwidth, networking throughput, and multi-node networking performance
Model Dev Experience: Develop and maintain internal GPU-enabled development environments (similar to GitHub Codespaces). You will ensure the team has seamless, high-performance "dev machines" optimized for model experimentation
Tool Development: Build and contribute to tools such as InferenceMAX and genai-bench to automate model evaluation and optimization
Deep Hardware Profiling: Use PyTorch Profiler and NVIDIA Nsight Systems to collect performance profiles, identify bottlenecks, and debug the NVIDIA compute/networking stack
Monitoring & Observability: Develop real-time dashboards and alerts to monitor system health, model startup times, and runtime performance
Continuous Integration: Automate performance testing via CI/CD pipelines to catch regressions in model setups before they hit production
Optimization Automation: Build tools to find the "Pareto frontier"—identifying the absolute best configuration (latency vs. cost vs. quality) for a given model and workload