Senior Performance Engineer

Samsung Semiconductor

Confirmed live 2 days ago High trust

Quick summary

Work type
On-site
Location
San Jose, CA
Salary
$138,000–$206,000 / yr
Posted
73 days ago
Freshness
Confirmed live 2 days ago

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $177k
This role $172k
$129k most similar roles pay here $223k

This role pays less than 54% of similar roles. Most pay $148,731–$205,362 — the shaded band above. At the midpoint, this role pays about $172k versus about $177k for comparable roles.

Based on 240 similar postings.

Employer

About Samsung Semiconductor

Samsung Semiconductor is the global semiconductor business unit of Samsung Electronics, designing and manufacturing memory chips, logic semiconductors, and foundry solutions for a broad range of applications.

Samsung Semiconductor currently has 49 open roles on FindRole.

Listed pay typically runs $163,000–$253,000 across 49 roles with salary data.

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At a glance

TL;DR · Senior Performance Engineer

Senior Performance Engineer The Senior Performance Engineer joins the STG group within the AGI Lab to address complex system-level challenges for AI/ML workloads. This role involves building and operating representative AI environments, including agentic workflows, distributed inference systems, and MoE deployments. You will collect workload traces and performance data to identify bottlenecks across compute, memory, communication, and scheduling resources while evaluating how emerging workloads interact with accelerator architectures. The position requires proficiency in Python and C++, along with experience using Nsight Systems and Nsight Compute to optimize workloads on NVIDIA GPU platforms. Candidates must possess a deep understanding of LLM inference and training systems, as well as familiarity with frameworks like PyTorch, vLLM, SGLang, TensorRT-LLM, DeepSpeed, Ray, or Megatron-LM. You will collaborate on hardware-software co-design to improve the performance and scalability of next-generation AI infrastructure.

What you'll do

  • Build and operate representative AI environments including agentic workflows, distributed inference systems, and MoE deployments.
  • Collect workload traces and performance data from real-world AI applications to characterize behavior.
  • Develop representative benchmarks and identify performance bottlenecks across compute, memory, communication, and scheduling resources.
  • Evaluate the impact of runtime, memory hierarchy, interconnects, and accelerator architecture on application performance.
  • Perform hardware-software co-design analysis for next-generation AI platforms in collaboration with engineering teams.
  • Profile and optimize AI workloads on NVIDIA GPU platforms using tools like Nsight Systems and Nsight Compute.
  • Analyze the performance implications of emerging workloads such as long-context reasoning and disaggregated inference.

What we're looking for

  • MS or PhD in Computer Science, Computer Engineering, Electrical Engineering, or a related field.
  • BS with 5+ years of experience in performance engineering, AI systems, distributed systems, or high-performance computing.
  • MS in Computer/Electrical Engineering or Computer Science with 3+ years of relevant working experience is preferred.
  • PhD degree with 0+ years of relevant working experience is preferred.
  • Strong understanding of LLM inference and training systems.
  • Strong understanding of NVIDIA GPU architecture, including compute, memory hierarchy, communication, and system-level bottlenecks.
  • Hands-on experience profiling and optimizing AI workloads on NVIDIA GPU platforms using Nsight Systems and Nsight Compute.
  • Proficiency in Python and C++.

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