Lead Infrastructure Engineer - Hardware and Systems Engineer

JPMorganChase•Jersey City, NJ

13d

About The Position

Assume a vital position as a key member of a high-performing team that delivers infrastructure and performance excellence. Your role will be instrumental in shaping the future at one of the world's largest and most influential companies. As a Lead Infrastructure Engineer at JPMorganChase within the Infrastructure Platforms, you apply deep knowledge of software, applications, and technical processes within the infrastructure engineering discipline. Continue to evolve your technical and cross-functional knowledge outside of your aligned domain of expertise. JPMorganChase is seeking a Hardware Engineer for end-to-end design of hardware platforms supporting our internal cloud and PSI environments. This role blends deep mechanical engineering fundamentals with strong program execution and vendor partnership. The ideal candidate is equally comfortable modeling and testing as they are coordinating cross-functional stakeholders, managing schedules and risk, and chassis integration details. If you enjoy turning complex, multi-constraint problems into practical, high-quality hardware designs, this role is for you.

Requirements

5+ years building and improving hardware products; a mechanical or electrical engineering background.
Strong experience partnering with OEM, ODM, and JDM manufacturers across design, build, and delivery.
Solid understanding of production rack environments spanning networking and compute hardware, plus power delivery and cooling behavior.
Able to apply thermodynamics in a datacenter context and confidently represent engineering positions in supplier design reviews.
Working knowledge of Open Compute and rack standards tied to heat rejection and voltage delivery.
Familiarity with PCIe, Redfish, and OpenBMC.
Hands-on Linux (Red Hat) server experience is required, along with Python or strong Bash scripting skills.
Hands-on experience with Llama API.
Experience owning BOMs and serving as a day-to-day interface with hardware suppliers, aligning cross-functional stakeholders to drive decisions and execution.
Self-directed, pragmatic, and comfortable diving into details.
Driven by engineering rigor, data-backed decisions, and continuous improvement.
Experience using enterprise-authorized AI tools to support infrastructure engineering workflows, with disciplined validation practices and strong awareness of data sensitivity.
Proven ability to review AI-assisted outputs before implementation, escalate uncertainty appropriately, and ensure outcomes meet resiliency, security, and auditability expectations.

Nice To Haves

Good knowledge of hardware specs.
Cloud on top of physical server experience.
Building automation and certifying servers for internal cloud.

Responsibilities

Partner with lines of business to capture requirements and translate them into end-to-end system designs.
Drive continuous improvement by redesigning or replacing components that fall short of performance, reliability, or cost targets, and plan/execute the retirement of legacy platforms.
Leverage working knowledge of board, server, and rack architectures with a strong understanding of datacenter integration.
Support the full lifecycle—from prototype builds and lab validation through production release.
Own day-to-day engagement with OEM/ODM/JDM partners, ensuring tight technical alignment and proactive escalation management.
Define milestones, acceptance criteria, and delivery expectations, and hold suppliers accountable for schedule, quality, and delivery commitments across the board and server development lifecycle.
Act as the technical bridge between requirements and shipped solutions across hardware and software stakeholders.
Communicate fluently at the hardware layer (e.g., PCIe topology, airflow and liquid-cooling dynamics) and connect design decisions and tradeoffs to network, operating system, and application requirements.
Uses enterprise-authorized AI capabilities within the work environment to accelerate infrastructure analysis and design documentation, validating outputs and handling operational data according to sensitivity and security requirements.
Applies reuse-first, AI-assisted practices within delivery and automation routines to identify recurring issues and validate remediation options, ensuring changes are traceable/auditable and aligned to resiliency and security expectations.