Senior Staff Engineer, AI Workloads & Storage

Samsung Semiconductor

Confirmed live today High trust

Quick summary

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

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $217k
This role $245k
$160k $316k
below market most similar roles pay here above market

This role pays more than 68% of similar roles. Most pay $177,737–$256,237 — the blue band above. At the midpoint, this role pays about $245k versus about $217k 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 36 open roles on FindRole.

Listed pay typically runs $163,000–$253,000 across 36 roles with salary data.

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At a glance

TL;DR · Senior Staff Engineer, AI Workloads & Storage

The Senior Staff Engineer - AI Workloads & Storage joins the Technology Enabling Development Lab to develop host interface firmware at the intersection of system software and flash management. This role focuses on building next-generation NAND/SSD storage systems optimized for large-scale AI workloads, specifically addressing bottlenecks in moving model weights and KV cache through the storage hierarchy. You will characterize production LLM inference, RAG, and training workloads to drive architecture decisions, identify optimal data placement using NVMe Flexible Data Placement and streams, and lead performance analysis across the Linux storage stack. Required skills include proficiency in Python and systems languages like C/C++, Rust, or Go, alongside deep knowledge of NAND internals, flash-translation layers, and tools like perf, ftrace, and eBPF to improve latency and throughput.

What does a Engineer earn in California?

Median $199500 from 103 postings across 31 companies.

See salary data

What you'll do

  • Profile production and emerging LLM inference, RAG, and training workloads to quantify I/O, bandwidth, and latency demands.
  • Translate workload findings into concrete storage and memory-hierarchy design decisions.
  • Analyze access patterns to determine optimal data placement using NVMe Flexible Data Placement and streams.
  • Lead deep-dive performance analysis across the inference runtime, Linux storage stack, and underlying hardware.
  • Build and evaluate transactional and system-level models to de-risk architectural decisions before hardware exists.
  • Develop proof-of-concept implementations for key customers to demonstrate data-path and tiering wins.
  • Set technical direction by making build-vs-buy calls and establishing benchmarking methodologies.
  • Engage with industry standards bodies like SNIA and MLCommons to align products with market trends.

What we're looking for

  • Bachelor's degree with 15+ years of experience, Master's with 13+ years, or PhD with 10+ years of relevant industry experience.
  • 10–15+ years of experience in systems, storage, or ML-systems software architecting for performance, reliability, or cost.
  • Demonstrated technical leadership in setting direction, driving cross-team decisions, and mentoring senior engineers.
  • Working knowledge of modern AI inference, specifically transformer architectures, attention, KV cache, and batching.
  • Deep understanding of Linux storage stack (block layer, I/O scheduling, NVMe) and NAND/SSD internals (FTL, garbage collection).
  • Hands-on performance analysis skills using tools such as perf, ftrace, eBPF, blktrace, and fio.
  • 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).

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