Job Opportunities API

The Public Ledger of Openings

← Back to the ledger

Head of Infrastructure, Stealth Edge AI Co

Montauk Capital
CompanyMontauk Capital
CategoryData & Analytics
LocationNew York City
RemoteHybrid
EmploymentNot stated
LevelDirector
SalaryNot stated by the employer
Posted6 May 2026
Last verified9 Aug 2026
SourceEmployer ATS (ashby)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
Head of Infrastructure Full Time, NYC / NJ Area Preferred About Montauk Capital Montauk Capital https://montaukcap.com builds and backs companies at the forefront of the Electron Economy, the generational shift towards electrified, intelligent technologies reshaping industries and driving unprecedented demand for energy. Our team combines deep investing acumen with decades of operating experience to give founders the strategic clarity and hands-on support that accelerates the building of enduring companies of consequence. About Stealth Edge AI Co Co-founded by Montauk Capital, Stealth Edge AI Co is a pre-seed venture specialized in modular, metro-edge AI capabilities. By leveraging existing infrastructure for inference deployment, Edge AI provides low-latency, SLA-guaranteed performance across diverse GPU SKUs and colocation environments. Our technology intelligently routes traffic based on demand proximity and real-world network limitations, bypassing the heavy power and infrastructure requirements of traditional hyperscalers. Currently initiating operations with pilot nodes in NYC, we are executing a city-by-city expansion strategy with plans for a broader multi-metro rollout. About the Role We’re building the automation, orchestration, and monitoring layer that unifies disparate metro edge GPU nodes into a single software-managed compute platform. You’ll own the definition, design, implementation, and execution of the hardware and infrastructure buildout, executing strategy across edge data center requirements, GPU selection, supply chain, technical implementation, operational maintenance and deployment as we scale. You’ll take the foundational groundwork and execute across the entire hardware and infrastructure side of our company, transforming our roadmap into production scale compute for AI inferencing. You’ll ensure the GPU clusters deliver on customer requirements, are highly-available, and will be the hands on expert for the hardware side of our business. Most importantly, you’ll turn our high-level plans into real, technical execution, and will play a key role in making supply chain decisions about infrastructure and how we deploy, scale, and support it. What You’ll Do - Own GPU infrastructure design and implementation details from planning through deployment - Own hardware selection, configuration, and deployment across early compute infrastructure - Help turn early technical groundwork into a functioning deployed system - Own the GPU roadmap we use to entice customers and build partnerships - Deploy, operate, and tune GPU clusters for both bare-metal and internal software stack - Own resilient networking implementation from each site to the cluster, including a robust OOB network for constant monitoring and management - Manage deployments at production scale - Interface with site ops on power, cooling, and connectivity - Build the automation and monitoring stack for distributed edge nodes - Own the supply chain for all infrastructure gear - Manage third party hardware vendors on provisioning, maintenance and break-fix support What You’ll Bring You’re a strong infrastructure engineer experienced with hardware deployment, data center environments, GPU selection, systems setup and design. You can manage the implementation details end to end and have ownership over the entire process. If AI infrastructure is your jam and you've built systems in production, we want to talk. - Strong infrastructure engineering experience and systems-level technical judgment - Experience deploying or managing compute infrastructure in real-world environments - Experience with data center, hardware, or GPU-based systems implementation - Experience owning GPU provisioning, hardware selection, and systems configuration - GPU scheduling and orchestration specifics: GPU type awareness, memory management, topology considerations, placement strategies for multi-GPU jobs, and fragmentatio