Technical Marketing Engineer (TME) for AI & On-Premises Object Storage (AI)

Location: Hybrid, with an average requirement of 2 days per week from an HPE office in Durham, North Carolina; Andover, Massachusetts; or Spring, Texas, United States.

Salary: Annual base salary of USD 178,000–339,000 in Massachusetts or USD 157,000–361,000 in North Carolina and Texas. Variable incentives may also be offered.

Company

hirify.global is a global edge-to-cloud technology company delivering infrastructure and data solutions from edge to cloud.

What you will do

• Design and maintain demonstration environments, infrastructure blueprints, hardware sizing guides, and validated reference architectures combining on-premises object storage with AI compute infrastructure.
• Develop AI/ML benchmark workflows, proof-of-concepts, and Jupyter notebooks for data preparation, ingestion, checkpointing, and large-scale unstructured data processing.
• Create whitepapers, deployment guides, solution briefs, competitive analyses, technical videos, and other technical content.
• Deliver technical enablement for Solutions Architects, Sales Engineers, channel partners, and customers.
• Evaluate storage I/O behavior, bottlenecks, scaling limitations, and workload performance using benchmarking tools and custom load generators.
• Provide customer-driven feedback to Product Management and Storage Engineering, mentor engineers, and contribute to software systems design and infrastructure automation.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Information Systems, or a related discipline, or equivalent practical experience.
• Typically 10+ years of experience in technical marketing, systems engineering, storage engineering, infrastructure architecture, or a related technical field.
• Deep experience with S3 API-based object storage, including bucket policies, lifecycle management, object locking, and multi-tenant data isolation.
• Strong understanding of data lakehouse architectures and Apache Iceberg, Delta Lake, and Parquet.
• Practical experience designing or operating distributed storage systems, including erasure coding, replication, metadata management, and cluster scaling.
• Experience with AI/ML training workflows and PyTorch, TensorFlow, Ray, or Hugging Face Transformers.
• Proficiency in Python or Go for infrastructure automation, API integrations, developer tooling, or systems development.
• Demonstrated success in technical writing, public speaking, technical video creation, customer enablement, or translating complex infrastructure topics for diverse audiences.
• Knowledge of AI data ingestion, data preparation, checkpointing, caching, storage throughput, enterprise hardware sizing, GPU clusters, storage fabrics, InfiniBand, RoCE/RDMA, 100GbE–400GbE Ethernet, Docker, Kubernetes, CSI, fio, warp, mdtest, or custom workload generators; ability to analyze performance data and communicate optimization opportunities clearly.
• Excellent collaboration, mentoring, presentation, technical writing, and stakeholder management skills, with the ability to translate complex infrastructure and AI/ML concepts into validated solutions and customer guidance.

Culture & Benefits

• Hybrid work model with flexibility to manage work and personal needs.
• Health, financial, and emotional wellbeing benefits for employees and their loved ones.
• Personal and professional development programs supporting career growth and knowledge expertise.
• Inclusive workplace with accessibility accommodations available during the application and interview process.