Drimva

Samsung

Staff Engineer, GPU Front-End Infrastructure/ Methodology

$151K–$252K

3900 N Capital of Texas Hwy, Austin, TX, USA +1 · On-site · Staff · Full-time · Posted Oct 9, 2026

Location
3900 N Capital of Texas Hwy, Austin, TX, USA
3655 N 1st St, San Jose, CA, USA
Experience
5+ years with a Bachelor’s Degree, or 3+ years with a Master’s Degree, or 1+ years with a Ph.D.
Education
Bachelor’s Degree in Computer Science/Engineering, Master’s Degree, or Ph.D.
Visa sponsorship
Applicants must be authorized to access information subject to U.S. export control laws or eligible for government authorization.

About the job

Own and advance RTL front-end implementation methodologies and infrastructure for GPU and SoC designs, enabling scalable design and verification workflows.

What you'll do

  • Develop and improve front-end RTL methodologies, guidelines, and quality practices aligned with performance and power goals.
  • Own and evolve RTL flows and build systems, including support for agentic AI workflows and related CI/CD, metrics, web application, and open-source tool initiatives.
  • Partner with design, verification, CAD, and physical design teams to remove workflow friction and lead methodology projects.

What they ask for

  • Hands-on experience designing and building agentic AI tools and workflows.
  • Proficiency in Unix/Linux programming, including Python, C++, Perl, Ruby, Go, and shell scripting; deep Linux system expertise.
  • Experience with DevOps, build automation and systems, including GNU Make; container environments and orchestration, including Docker, Kubernetes, and OpenShift.
  • Strong analytical, debugging, communication, documentation, and cross-functional collaboration skills.

Nice to have

  • Familiarity with RTL design concepts and testbench environments.

Pay

$151,000 and $251,800 USD base salary

Tools

Unix/Linux, Python, C++, Perl, Ruby, Go, GNU Make, Docker

AI focus

Design and build agentic AI tools and workflows; support an agentic AI workflow in the RTL flow and build system.

Model-written summary · the original posting is authoritative

First seen Oct 28, 2026 · Last verified Nov 4, 2026 (UTC)