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Principal Software Engineer – E2E Performance, Goodput
Job in
California, Moniteau County, Missouri, 65018, USA
Listed on 2026-08-02
Listing for:
Jobtailor
Full Time
position Listed on 2026-08-02
Job specializations:
-
Software Development
AI Reliability/ Performance Engineer
Job Description & How to Apply Below
- Drive performance characterization work streams with engineering teams of key CSP/hyperscale customers — ensuring they understand platform performance expectations, profiling methodology, and tuning options for their specific workloads
- Gather and synthesize CSP performance feedback — identify gaps between expected and actual throughput, and champion optimization priorities back into NVIDIA's CUDA, NCCL, driver, and firmware teams
- Ensure key open-source performance and stress tools (e.g., STREAM, GPU Burn, GPU BLAST) are updated and validated for the latest NVIDIA rack‑scale systems, GPU architectures, and CPU platforms — so customers and internal teams have reliable baseline measurements from day one
- Work closely with CSPs to ensure their own performance and validation tooling reflects the latest GPU capabilities, memory hierarchy changes, and platform‑specific tuning parameters
- Conduct cross‑CSP performance comparison and pattern analysis — identify configuration, software, or workload differences that explain performance gaps between deployments
- Collaborate with CSPs to ensure performance‑related integration work (profiling infrastructure, benchmark harnesses, config validation) is ready ahead of deployment milestones
- Define test strategies and tooling requirements for performance validation — both for NVIDIA internal certification and customer acceptance
- 15+ years of experience in systems performance engineering, ideally in GPU/HPC/ML infrastructure.
- BS or MS in Computer Science, Computer Engineering, or related field (or equivalent experience)
- Proficiency in GPU workload profiling: nsight systems, nsight compute, DCGM metrics, or equivalent instrumentation
- Understanding of distributed training performance dynamics: computation/communication overlap, pipeline bubbles, memory bandwidth utilization, collective efficiency
- Statistical methods for performance analysis: regression detection, confidence intervals, A/B comparison at scale
- Understanding of how the full software stack impacts performance: driver overhead, collective algorithm selection, memory allocation, scheduling, firmware power management
- Strong data analysis and visualization skills (Python, pandas, dashboards).
- Customer obsession — genuine passion for understanding why customers aren't achieving expected performance and driving solutions
- Ability to communicate performance findings to both deep technical audiences and executive leadership
- Demonstrated success influencing multiple engineering teams to prioritize performance improvements
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