Senior Staff Engineer, AI Workloads & Storage

Samsung Semiconductor

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
San Jose, CA
Salary
$189,000–$301,000 / yr
Posted
15 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $214k
This role $245k
$157k most similar roles pay here $316k

This role pays more than 67% of similar roles. Most pay $172,500–$254,750 — the shaded band above. At the midpoint, this role pays about $245k versus about $214k 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 Staff Engineer, AI Workloads & Storage

Senior Staff Engineer - AI Workloads & Storage joins the Technology Enabling Development Lab to bridge the gap between AI inference systems and storage software. This role focuses on optimizing NAND and SSD storage for large-scale AI demands, specifically addressing bottlenecks in moving model weights and KV caches through the storage hierarchy. You will characterize production LLM workloads, determine optimal data placement using NVMe Flexible Data Placement and streams, and lead performance analysis across the Linux storage stack and hardware. The position requires expertise in transformer architectures, NAND flash-translation layers, and tools like perf, ftrace, and eBPF. Candidates must be proficient in Python and a systems language such as C/C++, Rust, or Go. You will also engage with industry standards like SNIA and MLPerf to shape the future of AI data paths.

What does a Engineer earn in California?

Median $208000 from 94 postings across 22 companies.

See salary data

What you'll do

  • Profile production and emerging LLM inference, RAG, and training workloads to determine I/O, bandwidth, and latency requirements.
  • Translate workload characterization findings into concrete storage and memory-hierarchy design decisions.
  • Determine optimal data placement on flash using technologies like NVMe Flexible Data Placement and streams.
  • Conduct deep-dive performance analysis across inference runtimes, Linux storage stacks, and hardware components.
  • Build and evaluate transactional and system-level models to de-risk architectural decisions before hardware production.
  • Engage with industry standards and open ecosystems like SNIA and MLPerf to shape technical direction.
  • Set technical roadmaps, establish benchmarking methodologies, and mentor engineers across the organization.
  • Partner cross-functionally with product, hardware, and research teams to move architectures from concept to deployment.

What we're looking for

  • Bachelor's degree with 15+ years experience, Master's with 13+ years, or PhD with 10+ years of relevant industry experience.
  • 10 to 15+ years of experience in systems, storage, or ML-systems software.
  • Demonstrated technical leadership and cross-team influence to drive decisions and mentor senior engineers.
  • Working knowledge of modern AI inference, specifically transformer architectures and memory/compute trade-offs.
  • Deep understanding of the Linux storage stack and NAND/SSD internals including flash-translation layer and performance analysis tools.
  • Fluency in Python plus a systems language such as C, C++, Rust, or Go.
  • MS or PhD in Computer Science, Electrical/Computer Engineering, or a related field (preferred).
  • Experience with inference stacks, GPU data movement frameworks, SSD firmware, and system modeling (preferred).

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