Groq
Sr./Staff Forward Deployed Engineer
$270K–$402K
Dallas, United States +2 · On-site · Staff · Full-time · Posted Sep 22, 2026
- Location
- Dallas, United States
New York, United States
San Francisco, United States - Experience
- 4+ years
- Visa sponsorship
- For U.S. postings, candidates must qualify as U.S. Persons for export control purposes or otherwise be eligible for an applicable export license. For non-U.S. postings, candidates must meet relevant export control eligibility criteria.
About the job
Leads hands-on customer engagements to design, deploy, validate, and support production AI infrastructure across Groq and GPU platforms, while turning field solutions into reusable tools and platform improvements.
What you'll do
- Own customer deployments from discovery and architecture through cluster bring-up, validation, production readiness, and operational handoff.
- Troubleshoot across Linux, compute, networking, storage, orchestration, and workload layers; build code and automation to resolve issues.
- Develop reusable deployment patterns, tools, documentation, and customer feedback for platform and product improvements.
What they ask for
- Hands-on experience with cloud infrastructure, AI infrastructure, HPC systems, large-scale platforms, or comparable production environments.
- Strong Linux and distributed-systems fundamentals and practical experience with Kubernetes, Slurm, bare-metal environments, or comparable platforms.
- Technical depth in at least one infrastructure area, plus working knowledge of AI training and inference workloads.
- Strong Python, Go, Bash, or equivalent scripting/programming skills.
Nice to have
- Experience operating large-scale accelerator infrastructure at a neocloud, hyperscaler, AI infrastructure provider, HPC environment, or frontier AI company.
- Hands-on NVIDIA GPU infrastructure experience, including technologies such as CUDA, NCCL, NVLink/NVSwitch, or InfiniBand.
Pay
Staff: $270,400 - $365,800 (total cash salary); Sr. Staff: $341,400 - $401,600 (total cash salary)
Tools
Linux, Kubernetes, Slurm, Python, Go, Bash, NVIDIA GPU, GroqCloud
AI focus
Deploys infrastructure for AI training and inference workloads, including accelerator clusters; assesses workload behavior, performance, and infrastructure bottlenecks.
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