Pure Storage
Senior Pre-Sales Systems Engineer, Higher Education (Texas)
$141K–$212K
Remote, Texas +1 · Remote · Senior · Full-time · Posted Oct 29, 2026
- Location
- Remote, Texas
Texas, United States - Experience
- 8 or more years of relevant experience across technology, solution design, and customer-facing communication.
- Education
- BS in CS, EE, another technical degree, or equivalent experience.
About the job
Leads pre-sales technical engagements for Texas higher-education accounts, designing and positioning solutions that support research, HPC, AI, data management, and institutional goals.
What you'll do
- Build technical and financial cases, tailor discussions to institutional stakeholders, and connect solutions to research and operational outcomes.
- Lead evaluations, benchmarks, proofs of concept, configurations, and technical responses to RFIs, RFPs, and RFQs.
- Support research data and workloads across HPC and AI pipelines, retention, governance, compliance, and archiving.
What they ask for
- 8 or more years of relevant experience across technology, solution design, and customer-facing communication.
- Pre-sales or customer-facing technical experience, plus experience with higher education, research computing, academic medical centers, or the public sector.
- Familiarity with HPC and AI environments, including parallel and distributed file systems, GPU-attached storage, schedulers such as Slurm, and research data pipelines.
- Broad technical depth across two or more listed domains; value-based selling track record; BS in CS, EE, another technical degree, or equivalent experience.
Nice to have
- Knowledge of storage architectures and file systems; Linux, NFS, networking, virtualization, cloud, containers, databases, or analytics platforms.
- Experience with grant-funded procurement, consortium purchasing, or public sector contract vehicles.
Pay
$141,400 — $212,100 USD annual base salary
Tools
HPC, AI, Slurm, FC, iSCSI, NAS, SAN, Kubernetes
AI focus
Supports customer research and institutional AI environments, including GPU-attached storage and data pipelines for research workloads.
Model-written summary · the original posting is authoritative