Principal Systems Engineer – Quantum Computing Systems

Summary We are seeking a Principal Systems Engineer to play a critical role in aligning engineering execution with scientific and machine-level progress in the development of large-scale quantum computers. This role sits at the intersection of quantum science, hardware engineering, and control software, with a primary mission to make the system coherent, buildable, and integrable as it evolves. Unlike traditional product environments, many system requirements in quantum computing are discovered through experimentation, not defined upfront. Success in this role requires deep collaboration with scientists, rapid learning, and the ability to introduce structure only where it accelerates progress. The ideal candidate brings extensive experience building complex hardware–software systems (e.g., aerospace, EVs, robotics, advanced instrumentation) and is motivated to apply systems engineering rigor in a learning-driven R&D environment, pairing closely with internal quantum experts. This is a technical leadership role with broad influence across teams. Authority comes from clarity, usefulness, and trust — not from gatekeeping or heavy process. Core Mission Bridge the gap between engineering tasks and quantum machine milestones Make system architecture, integration status, and technical risk visible and actionable Enable scientists and engineers to move faster together by reducing ambiguity, friction, and rework Help the organization evolve from ad-hoc integration to disciplined, scalable system development — without slowing discovery Responsibilities 1. System Understanding Through Scientific Partnership Work closely and continuously with quantum scientists to understand how the machine is actually operated, tuned, and debugged in practice. Spend significant time in the lab, observing experiments and participating in scientific discussions to absorb tacit system knowledge. Treat scientists as primary system knowledge holders, approaching requirement gathering as a learning and synthesis exercise. Build trust by accurately reflecting scientific intent and constraints in system models, requirements, and architectural decisions. 2. Requirements Co-Evolution & Traceability Facilitate the co-evolution of system requirements as the machine progresses: Start with lightweight, provisional requirements Explicitly document uncertainty, assumptions, and open questions Refine requirements as experimental results and understanding improve Translate scientific goals (e.g., performance, stability, operability) into actionable engineering requirements while preserving necessary flexibility. Establish traceability between: machine-level goals subsystem requirements engineering deliverables (e.g., JIRA epics) Ensure engineers understand the intent behind requirements, not just the wording. 3. System Architecture & Integration Leadership Develop and maintain a livi...