Pure Storage
Senior Pre-Sales Systems Engineer, Higher Education (IN/MI)
$141K–$212K
Remote, Indiana +3 · Remote · Senior · Full-time · Posted Oct 29, 2026
- Location
- Remote, Indiana
Remote, Michigan
Indiana, United States
Michigan, 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 strategy for higher education accounts, designing and presenting solutions that support research computing, HPC, AI workloads, and institutional data needs.
What you'll do
- Develop account-specific technical and financial business cases, connecting solutions to research and institutional outcomes.
- Lead evaluations, benchmarks, proofs of concept, system configurations, and technical responses to RFIs, RFPs, and RFQs.
- Support research data and workloads from instrument output through HPC and AI pipelines to long-term retention, while coordinating with account teams and partners.
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 and 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.
- BS in CS, EE, another technical degree, or equivalent experience.
Nice to have
- Knowledge of storage architectures and file systems; Linux, NFS, VMware, networking, cloud, containers, Kubernetes, or data-intensive applications.
- Experience with grant-funded procurement, consortium purchasing, or public sector contract vehicles.
Pay
$141,400 — $212,100 USD (base salary)
Tools
Slurm, FC, iSCSI, NAS, SAN, Linux, NFS, Kubernetes
AI focus
Supports customer AI and machine-learning research infrastructure and workloads, including GPU-attached storage and data pipelines.
Model-written summary · the original posting is authoritative