PsiQuantum is on a mission to build the first real, useful quantum computers capable of delivering world-changing applications. The company aims to construct a system with roughly 1 million qubits that supports fault-tolerant error correction within a scalable architecture, leveraging silicon photonics for high-volume manufacturing.
Requirements
- 5+ years professional software engineering; 3+ years in feedback-control, calibration, robotics, or analogous real-time domains.
- Fluency in Python or Julia, particularly in the context of scientific computing, AI/ML, or signal processing.
- Fluency in Rust and/or C++17+; comfortable writing lock-free, wait-free, and numerically stable code paths.
- Proven track record delivering production systems with real-time or ultra-low-latency requirements.
- Hands-on experience interfacing with hardware over PCIe, Ethernet, or custom serial buses; confident reading schematics & timing diagrams.
- Working knowledge of Linux internals (irq, cgroups, NUMA, hugepages) and high-performance networking stacks.
- Strong foundation in one or more of: quantum information processing, artificial intelligence, robotics, control theory, optimization, signal processing, or experimental physics.
Responsibilities
- Architect and implement calibration and adaptive control software in Rust (no-std + async), C++20, and Python targeting x86, GPUs, DPUs, and embedded MCUs.
- Design closed-loop optimization algorithms (e.g., Bayesian, stochastic gradient descent, ADAM) that maximize system performance and stability under drift and environmental noise.
- Integrate with high-speed fabrics: libfabric, RDMA (RoCEv2/GDR), and FPGA PCIe DMA endpoints.
- Build scalable simulation/emulation harnesses (Python, Rust, cuQuantum) to de-risk algorithms before system bring-up.
- Implement hooks for observability metrics, traces, SLOs and partner with sibling teams to integrate into our observability infrastructure.
- Collaborate daily with physics, validation, firmware, and FPGA teams to co-design HW/SW interfaces and define calibration priorities.
- Conduct rigorous code reviews, design reviews, and root-cause analyses; uphold our bar for deterministic, safety-critical code.
Other
- B.S. in CS, EE, Physics, or related discipline (or equivalent practical experience).
- M.S./Ph.D. focused on numerical optimization, control systems, photonics, computer science, or quantum information.
- Demonstrated experience accelerating numerical Python workflows using Mojo, Jax, PyTorch, or similar.
- Experience with sub-nanosecond clock-sync protocols.
- Familiarity with FEC and/or QEC (forward error correction / quantum error correction) concepts, stabilizer circuits, fusion-based architectures, or tensor-network simulation.
