Senior Software Engineer (Quantum Computing)

About this role

Infleqtion is on a mission to commercialize atom-based quantum technologies that deliver orders-of-magnitude improvements in sensing and computing applications. We are seeking self-motivated, energetic individuals with exceptional problem-solving and technical skills to help drive our Quantum Computing mission forward. We build quantum computers. You will write the software that runs them. This is a production-first, high-coding role — roughly 90% of your time will be spent writing and owning code, not reviewing or directing others who do.

You will work directly alongside physicists and hardware engineers, converting complex scientific requirements into software that controls real quantum machines. If you are looking to step back from the keyboard, this is not that role. Requirements You will Write, own, and ship production backend services and control frameworks that directly operate and calibrate our neutral atom quantum computers — if it breaks, you fix it.

Debug and support every layer of the control stack — from real-time embedded kernels to distributed services — without handing it off when it gets hard. Build reliable tooling that lets physicists and hardware engineers run experiments independently, which means understanding what they need well enough to build it without a lengthy translation process. Develop and maintain CI/CD pipelines, containerized deployments using Docker and Kubernetes, and Python packages with C++ or Rust extensions in a Linux-native environment.

Work hands-on with experimental data — time-series, SQL and non-relational databases, and realtime image analysis and object detection from live quantum systems. You have 6+ years of backend software engineering; a degree in Computer Science, Physics, Applied Mathematics, or a field where writing code to solve hard quantitative problems was the norm, not the exception. Production-tested Python and hands-on C++ or Rust experience — both is a strong advantage; candidates who bring all three move to the front.

A track record of rigorous engineering discipline: comprehensive testing, clean revision control, operational reliability, maintainable systems, thorough documentation, and a reflex for code health — not as process, but as habit. Real experience with scientific or experimental data pipelines — NumPy, SciPy, PyTorch, time-series storage, and ETL — ideally in a context where data quality directly affected outcomes. A science or mathematics background, or verifiable experience embedded with scientists translating research requirements into production software — you can hold your own in a conversation about physics.