Principal Systems Engineer - Quantum Computing Systems

Principal Systems Engineer – Quantum Computing Systems

Boston, MA USA

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:
• Subsystem requirements
• Engineering deliverables (e.g., JIRA epics)
• Ensure engineers understand the intent behind requirements, not just the wording.
• Develop and maintain a living system architecture covering:
• Control electronics and firmware
• Control software and orchestration layers
• Produce clear, accessible architecture diagrams that reflect reality and evolve with the system.
• Identify missing architectural elements, poorly defined interfaces, and integration risks early.
• Lead system‑level trade studies and technical decision‑making in partnership with engineering and scientific leaders.
• Ensure architecture reflects machine milestones and not just organizational boundaries.

4. Integration, Test Strategy, and Machine Protection

• Define and drive a system integration and test strategy appropriate for an evolving R&D machine.
• Help ensure engineering testbeds match machine configurations as closely as possible.
• Push integration testing upstream so that machines are not used as primary test platforms.
• Partner with engineering teams to define validation criteria tied to real machine behavior.
• Reduce burden on lab teams by improving pre‑deployment testing and integration readiness.

5. Early Wins & Trust Building

• Deliver small, tangible improvements early that directly reduce friction for scientists and engineers, such as:
• Clarifying a recurring interface problem
• Creating a simple integration checklist
• Documenting a failure mode that saves days of debugging
• Use these wins to establish credibility and reinforce the value of systems engineering.
• Continuously gather feedback on what is helping vs. slowing teams down, and adapt approach accordingly.

6. Unlearning, Adaptation, and Process Design

• Actively identify where traditional systems engineering assumptions do not apply to quantum computing.
• Unlearn rigid models around fixed requirements, early design freezes, and linear development.
• Introduce process and structure incrementally, calibrated to system maturity.
• Champion systems thinking without dogma — prioritizing outcomes over formality.

7. Technical Leadership & Mentorship

• Serve as a technical leader and mentor for engineers and emerging systems thinkers.
• Help scale system knowledge through documentation, diagrams, and internal education.
• Contribute to leadership discussions on technical strategy, integration risk, and execution pacing.

What Success Looks Like

Within 6–12 months:

• Scientists feel supported by engineering integration work.
• Engineering efforts show clearer linkage to machine milestones.
• Integration issues are identified earlier and resolved faster.
• System architecture is widely referenced and trusted.
• Leadership has improved visibility into system readiness and risk.
• Machine milestones are achieved.

Qualifications

• 10+ years of experience in systems engineering or system architecture for complex hardware–software systems.
• Proven experience integrating multidisciplinary systems (hardware, software, controls).
• Strong background in requirements definition, interface management, and system integration.
• Ability to operate effectively in ambiguous, fast‑evolving R&D environments.
• Exceptional communication and collaboration skills.

Preferred Qualifications

• Experience in aerospace, automotive, robotics, advanced instrumentation, or similar domains.
• Familiarity with model‑based systems engineering (MBSE) concepts or tools.
• Experience working closely with research or experimental teams.
• Exposure to quantum technologies is a plus, but not required.

Personal Attributes We Value

• Intellectual humility and curiosity
• Comfort learning from domain experts
• Bias toward clarity and usefulness

Why This Role Matters

You will help build a machine that has never existed before — not by enforcing process, but by making the system understandable, integrable, and scalable. This role is foundational to turning scientific breakthroughs into reliable quantum computers.

QuEra is committed to cultivating a diverse work environment and is proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.