Principal Software Engineer – E2E Performance, Goodput

Jobtailor; California (MO); On-site; USD 150,000 - 210,000; Full time

Job summary

Jobtailor is seeking a seasoned Systems Performance Engineer to drive GPU/HPC/ML workload performance for CSP/hyperscale deployments. You will profile workloads, analyze bottlenecks, and champion optimizations across NVIDIA CUDA, NCCL, drivers, and firmware teams.

Responsibilities include coordinating with engineering teams, defining test strategies, and ensuring measurement tools remain up-to-date with the latest architectures and platforms.

Qualifications

• 15+ years of experience in systems performance engineering.
• BS or MS in Computer Science, Computer Engineering, or related field (or equivalent).
• Proficiency with GPU workload profiling tools and instrumentation.
• Understanding of distributed training performance dynamics.
• Statistical methods for performance analysis and AB comparisons at scale.
• Knowledge of how software stack affects performance: drivers, memory, scheduling, firmware powers.
• Strong data analysis and visualization skills (Python, pandas).
• Ability to communicate findings to technical and executive audiences.
• Proven track record influencing multiple engineering teams to prioritize performance improvements.

Responsibilities

• Drive performance characterization with engineering teams for GPU/HPC/ML workloads.
• Synthesize CSP performance feedback and champion optimization priorities across CUDA, NCCL, drivers, and firmware teams.
• Ensure performance tools (e.g., nsight tools, DCGM) are updated and validated for latest NVIDIA rack-scale systems and architectures.
• Collaborate with CSPs to align profiling and benchmarking tooling with platform capabilities.
• Define test strategies and tooling requirements for performance validation for internal and customer approval.

Skills

GPU profiling

Python

Pandas

Data visualization

Statistical analysis

Cross-team collaboration

Performance optimization

Executive communication

Education

BS in Computer Science or Engineering

MS in Computer Science or Engineering

Tools

Nsight Systems

Nsight Compute

DCGM Metrics

Job description

• 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

Requirements

• 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