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Salary
$220k – $250k per year
Location
Remote/Hybrid (Berkeley, United States)
Seniority
Senior · 7+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
We're on a mission to build the world's most powerful computers to help solve humanity's most important and pressing problems. We build and deliver integrated quantum-classical systems through our Quantum Cloud Services platform to help our custom...

About Us

At Rigetti, our mission is to build the world’s most powerful computers to solve the world’s most challenging problems. Rigetti is a pioneer in superconducting Qubits and full stack quantum computing systems. In addition to introducing the first multi-chip quantum processor, Rigetti has led the way in software systems for quantum computing, with a distributed operating environment for hybrid quantum-classical computing, and a state-of-the-art streaming characterization and measurement development and execution platform

We are seeking a Senior Software Engineering Manager to head the team responsible for software systems that enable the creation, delivery and maintenance of Rigetti’s quantum computers. This includes systems for device design, chip fabrication workflows, physics-informed data analysis, quantum gate-design, and quantum mitigation and error correction technologies. This also includes managing the team and supporting the development of software systems for quantum QPU and system characterization. This is a hands-on leadership role for someone who thrives at the intersection of state-of-the-art software engineering, hardware, and scientific computing.

Role Overview

    As a Senior Software Engineering Manager, you will lead the team responsible for software platforms that enable the design, fabrication, characterization, and measurement of Rigetti's quantum systems - with a strong emphasis on integrating into the engineering process.

    You will manage and develop a team of professional software engineers, and collaborate closely with quantum hardware teams, quantum control systems designers, chip designers, fabrication engineers, and algorithm researchers. You’ll be responsible for engineering and system design that spans the entire gamut of our foundry operations through the delivery of an integrated quantum computing system, and delivery of characterization and measurement software to customers.

    This is a unique opportunity to build software systems critical for the design and delivery of Rigetti’s quantum computers at a time of significant organizational momentum. The ideal candidate is passionate about accelerating our hardware development flywheel and can empower technical teams in complex scientific environments while creating structure, accountability, prioritization, and long-term sustainability.

Key Responsibilities

    Team Leadership & Organizational Development

  • Lead and mentor a cross-functional software engineering team spanning scientific tooling, analytics infrastructure, and fabrication-support systems.

  • Create stronger operational rigor around prioritization, execution, accountability, and project delivery.

  • Provide coaching, growth support, feedback, and career development for engineers across varying levels of seniority.

  • Help establish healthy engineering culture, team norms, and stronger collaboration practices.

  • Serve as a day-to-day thought partner to the VP of Software Engineering while helping create additional leadership bandwidth across the organization.

  • Participate in hiring and team scaling efforts as the organization grows.

  • Technical & Engineering Leadership

  • Partner closely with engineers to drive execution across scientific computing, fabrication tooling, analytics systems, and workflow automation.

  • Support development of internal tooling and data systems that connect design, fabrication, and device performance workflows.

  • Help modernize and operationalize internal tooling that has historically evolved organically through research and prototyping efforts.

  • Drive better long-term ownership, maintainability, and lifecycle planning for internally developed systems and infrastructure.

  • Help evolve engineering infrastructure and validation frameworks that improve scalability, reproducibility, and operational reliability across hardware development environments.

  • Support technical decision-making, architecture discussions, and engineering prioritization while remaining hands-on enough to engage credibly with the team.

  • Contribute technically when appropriate, particularly around debugging, architecture reviews, infrastructure improvements, or smaller engineering initiatives.

  • Cross-functional Collaboration

  • Work closely with quantum engineers, physicists, fabrication engineers, and hardware teams to understand operational pain points and evolving workflow requirements.

  • Help balance competing stakeholder requests while improving prioritization discipline and long-term planning.

  • Partner with fabrication teams as Rigetti continues evolving manufacturing and foundry operations across Berkeley and Fremont.

  • Support systems that connect experimental data, fabrication workflows, characterization tooling, and engineering analytics into scalable internal platforms.

  • Operational & Strategic Impact

  • Help transition the team from highly reactive support work toward more proactive engineering planning and scalable systems development.

  • Improve processes around technical debt management, prototype ownership, platform sustainability, and operational support models.

  • Create better visibility into engineering priorities, roadmap tradeoffs, and team capacity.

  • Support ongoing evolution of Rigetti’s scientific and engineering software ecosystem as the company scales hardware development efforts.

  • Oversee the development, and architecture of data platforms and modeling tools that combine design files, fabrication process data, and device performance metrics.

  • Empower the development and support for tools for design rule checking (DRC), layout-vs-schematic (LVS) checks, and process-aware design verification for quantum devices.

Qualifications

  • BS or equivalent degree in Software Engineering, Electrical Engineering, Physics, Applied Physics, or related field; MS or PhD a plus.

  • 7+ years of software engineering experience, including 2+ years in a people leadership role, whether formal management or equivalent tech lead capacity

  • Demonstrated experience building data systems, ideally in scientific computing or physics-informed models.

  • Demonstrated experience with software systems managing large datasets, metadata, and scalable data pipelines.

  • Experience with streaming architectures and tools (e.g., Kafka, Kinesis, Spark Streaming).

  • Proficiency in Python; familiarity with JavaScript or TypeScript. Comfort in a scientific computing environment is strongly valued.

  • Proficiency with AWS for development and production deployment and distributed computing.

  • People leadership ability, including coaching, performance management, and building team culture, whether formal management experience or demonstrated informal leadership.

  • Strong communication and stakeholder management skills; ability to represent the team's capacity and push back constructively on internal customer requests.

  • Hands-on technical ability; should be capable of making code contributions on an as-needed basis, even if day-to-day focus is leadership.

Nice-to-Have

  • Knowledge of quantum device physics, such as superconducting qubits, trapped ions, photonics, or quantum information science.

  • Knowledge of quantum control systems and low-level hardware/software interfaces, including cryogenic hardware, RF electronics, and qubit calibration.

  • Familiarity with fabrication processes in semiconductor, quantum, or other hardware/lab-based environments.

  • Familiarity with EDA tools, process design kits (PDKs), and chip layout software such as KLayout or Cadence.

  • Experience with scientific computing platforms, simulation tools, and scientific computing libraries/tools such as NumPy, SciPy, PyTorch, etc.

  • Experience applying machine learning to scientific, operational, or hardware systems for prediction, optimization, or anomaly detection.

  • Experience with compiled languages such as Go or Rust.

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