Forward Deployed Engineer (AI/HPC)

TL;DR

Forward Deployed Engineer (AI/HPC): Designing and deploying data architectures, integrations, and automation for AI and high-performance computing workloads with an accent on GPU orchestration, high-speed networking, and distributed storage performance. Focus on solving last-mile infrastructure challenges, debugging complex latency and throughput issues, and translating customer requirements into scalable product improvements.

Location: Remote, but must be based in the Dallas, Texas area or willing to relocate to Dallas; no exceptions.

Salary: $200,000–$240,000 base salary, plus health, retirement, and flexible time-off benefits.

Company

hirify.global provides a data platform that centralizes data through a unified namespace and enables decentralized, accelerated access for AI and high-performance computing workloads.

What you will do

• Embed with strategic customer accounts as the primary technical authority and resolve real-world infrastructure challenges.
• Design and implement data architectures that optimize AI/ML training pipelines and low-latency access across hybrid-cloud environments.
• Build custom integrations, automation scripts, and last-mile solutions using product APIs.
• Identify expansion opportunities and emerging customer workloads alongside the Named CSM.
• Translate field-discovered bugs and requirements into product improvements with Sustaining Engineering.
• Communicate technical findings and recommendations to data scientists, IT administrators, and executive stakeholders.

Requirements

• Must be based in the Dallas, Texas area or willing to relocate to Dallas.
• Deep expertise in GPU orchestration, AI/ML data pipelines, and high-performance file systems including NFS, SMB, and S3.
• Principal-level mastery of InfiniBand, RoCE, 100GbE+, distributed networking, routing, latency, and throughput troubleshooting.
• Production-grade programming experience with Python, Go, or C++.
• Strong Linux internals knowledge, including kernel tuning, storage performance, Kubernetes, and Docker.
• Ability to learn and deploy data platforms across heterogeneous storage and cloud providers.

Culture & Benefits

• High-autonomy, high-stakes work embedded directly with strategic customers.
• Close collaboration between field engineering and product engineering teams.
• Medical, dental, vision, life, and disability insurance plans.
• 401(k) plan and flexible time off.