Quantum Data Performance Engineer - Systems Integration

Responsibilities

Working closely across all pillars, the engineer will synthesize system-level insights and communicate findings to inform technical strategy and prioritize improvements. Collaborating across technical pillars to understand the requirements and operational characteristics of different subsystems within the quantum computer Designing, implementing, and refining data pipelines for historical monitoring of key subsystem performance metrics Conducting advanced statistical analysis and data mining to identify performance trends, top risks, and areas for improvement across subsystems Documenting and communicating the results of performance tracking and analysis to stakeholders, providing actionable insights to inform system strategy and risk mitigation Partnering with software engineering teams to develop, maintain, and enhance code infrastructure supporting data analysis and reporting Proactively identifying new metrics and risk indicators to be monitored, and collaborating to stand up new data pipelines and dashboards for visibility into subsystem health and performance Supporting cross-functional teams in root-cause analysis and troubleshooting by providing deep dives into subsystem data and performance history

Qualifications

Master’s Degree in Physics, Engineering, or related field. OR Bachelor’s Degree in Physics, Engineering, or related field AND experience in industry or in a research and development environment Experience with large-scale data analysis, statistical modeling, and visualization tools (e.g., Python, R, MATLAB, PowerBI). Experience building and maintaining automated data pipelines, databases, or cloud-based monitoring solutions. Ability to leverage AI tools to drive innovation and efficiency (e.g., performance modeling and analysis, research gathering, day to day task automation). Ability to work in an “AI-first” environment using modern AI tools to accelerate discovery through hardware development. Doctorate in Physics, Engineering, or related field OR Master’s Degree in Physics, Engineering, or related field AND proven experience in industry or in a research and development environment OR Bachelor’s Degree in Physics, Engineering, or related field AND demonstrated experience in industry or in a research and development environment OR equivalent experience. Leveraging artificial intelligence tools and techniques to enhance data tracking, analysis, and visualization. Strong communication skills and a track record of translating technical data into actionable recommendations for multi-disciplinary teams. Demonstrated ability to independently identify and address performance risks in complex engineering systems. Experience with superconductor/semiconductor physics, RF measurement techniques, and/or cryogenic systems. Ability to be flexible and adapt to new situations in a rapidly changing research environment. Experience in design and analysis of experiments. Experience with statistical process control methodologies.

Key Impact Areas

• Accelerates the deterministic maturation of fault-tolerant quantum hardware through rigorous subsystem benchmarking
• Mitigates systemic integration risks by establishing unified performance telemetry across disparate hardware pillars
• Facilitates the transition toward automated hardware optimization through the deployment of AI-ready data architectures
• Reduces the iteration friction between experimental physics and system-wide engineering through high-fidelity data loops
• Strengthens the reliability of quantum-classical hybrid systems by ensuring consistent monitoring of classical control interfaces
• Harmonizes multi-disciplinary R&D efforts by providing actionable data insights to both hardware and software teams
• Optimizes the lifecycle of quantum processors by identifying long-term performance trends and failure modes
• Supports the scaling of quantum adoption by establishing industry-standard metrics for system health and operational readiness
• Shortens the timeline for hardware troubleshooting through the application of advanced statistical root-cause analysis
• Improves the capital efficiency of deep-tech investments by providing data-driven validation of technical milestones
• Protects the integrity of the technology stack by proactively identifying emerging risks in cryogenic and RF subsystems
• Enables the strategic orchestration of hardware development roadmaps through centralized system-level performance visibility