Core Engineer - Hardware Systems (Multiple Levels)

Edgescale AI•Denver, CO

About The Position

We’re looking for a Core Engineer focused on hardware systems to build and evolve the physical edge platform that runs our private AI and data infrastructure in the real world. You’ll own key portions of the hardware lifecycle end-to-end—from rapid system design and component iteration through validation, testing and certification, procurement, and field reliability. This role has the authority to stop hardware designs, component choices, or shipments that would put reliability, safety, or security at risk. You’ll ensure the hardware provides a secure foundation for the platform, supporting secure boot, secure updates, and overall system integrity. You will lead silicon platform choices across CPU, GPU, networking, and accelerators, aligning hardware design with workload needs, software support, and vendor roadmaps. You’ll also design systems with clear cost targets, balancing performance and reliability against total cost across manufacturing, deployment, and fleet operation. This is a hands-on role for someone who thrives in a high-ownership setting and wants to build the infrastructure that makes real-world AI possible.

Requirements

6+ years building, validating, and shipping production hardware systems in real-world environments.
Experience with system-level engineering across compute platforms (CPU/GPU), memory/storage, networking, thermals, and power delivery (e.g., PCIe-based systems, high-performance GPUs, and high-speed networking).
Experience leading silicon and platform selection decisions in collaboration with software and infrastructure teams, including vendor engagement and roadmap alignment.
Strong engineering craft: crisp designs, structured validation, operational clarity, and clear documentation (e.g., schematics, BOMs, validation plans, and design reviews).
Comfort working in ambiguity and making sound trade-offs under real constraints, including cost targets, supply chain risk, and deployment timelines.
Ownership mindset: outcomes over tasks.

Nice To Haves

Rapidly iterating high-performance systems from prototype to production, including bring-up, validation, certification, and field sustainment.
Managing contract manufacturers and suppliers to deliver predictable quality, security, and cost outcomes at scale.
Hands-on experience with thermal design, power behavior, performance profiling, and failure analysis in high-performance systems.
Designing secure-by-default hardware platforms, including secure boot chains, hardware roots of trust (e.g., TPM), signed firmware, and secure update mechanisms.
Building validation, test automation, and manufacturing QA processes that scale across fleets while controlling total cost of ownership.

Responsibilities

Own hardware architecture and rapid iteration cycles across compute, GPU, memory, storage, networking, connectivity, thermal, and power subsystems.
Lead silicon platform selection across CPU, GPU, networking, and accelerators, aligning performance, software compatibility, vendor roadmaps, and lifecycle support.
Lead component selection and system integration, balancing performance, reliability, manufacturability, security, and total cost of ownership.
Define and execute validation plans for performance, thermals, power, stability, security, and field reliability under real operating conditions.
Ensure hardware provides a secure foundation for the platform, including support for secure boot, signed updates, hardware roots of trust, and system integrity.
Drive compliance, certification, and testing workflows as required for real-world deployments.
Own procurement execution with contract manufacturers and vendors, including quoting, timelines, quality controls, and issue resolution.
Maintain strong vendor relationships and manage suppliers as an extension of the engineering process, with authority to stop designs or shipments that introduce unacceptable risk.
Maintain crisp design documentation and decision records, using AI tools to accelerate analysis, trade studies, and iteration loops, then refining with engineering judgment.