Principal ML Systems Engineer – AI for Quantum

PsiQuantum•Milpitas, CA

About The Position

PsiQuantum’s mission is to build the first useful quantum computers—machines capable of delivering the breakthroughs the field has long promised. Since our founding in 2016, our singular focus has been to build and deploy million-qubit, fault-tolerant quantum systems. Quantum computers harness the laws of quantum mechanics to solve problems that even the most advanced supercomputers or AI systems will never reach. Their impact will span energy, pharmaceuticals, finance, agriculture, transportation, materials, and other foundational industries. Our architecture and approach is based on silicon photonics. By leveraging the advanced semiconductor manufacturing industry—including partners like GlobalFoundries—we use the same high-volume processes that already produce billions of chips for telecom and consumer electronics. Photonics offers natural advantages for scale: photons don’t feel heat, are immune to electromagnetic interference, and integrate with existing cryogenic cooling and standard fiber-optic infrastructure. In 2024, PsiQuantum announced government-funded projects to support the build-out of our first utility-scale quantum computers in Brisbane, Australia, and Chicago, Illinois. These initiatives reflect a growing recognition that quantum computing will be strategically and economically defining—and that now is the time to scale. PsiQuantum also develops the algorithms and software needed to make these systems commercially valuable. Our application, software, and industry teams work directly with leading Fortune 500 companies—including Lockheed Martin, Mercedes-Benz, Boehringer Ingelheim, and Mitsubishi Chemical—to prepare quantum solutions for real-world impact. Quantum computing is not an extension of classical computing. It represents a fundamental shift—and a path to mastering challenges that cannot be solved any other way. The potential is enormous, and we have a clear path to make it real. Come join us. Job Summary: The Quantum Architecture Software team at PsiQuantum builds the core software and data systems that underpin the study and simulation of quantum computer architectures and the AI infrastructure and applied systems that accelerate the development of fault-tolerant photonic quantum computers. We sit at the intersection of machine learning, quantum architecture research, and autonomous systems; developing the tools, models, and agent frameworks that let quantum architects move faster and think more clearly. Our work within the AI for Quantum initiative spans three interrelated areas: training and deploying ML models that operate directly on quantum systems (decoders, scorers, and RL agents for architectural optimization); building scientific agent systems that augment human researchers in exploring large, complex design spaces; and enabling AI-driven control, test, and validation infrastructure for quantum hardware, software, and systems. The team is small, cross-domain, and R&D-oriented. We build things that don't exist yet.

Requirements

Python, with strong software engineering fundamentals
PyTorch — training, fine-tuning, and deploying models in production
Distributed training on GPU clusters (multi-node, NCCL, Slurm or equivalent)
GPU kernel development — CUDA, ROCm, or Triton; sufficient depth to debug performance and understand memory hierarchy
AI/ML infrastructure: experiment tracking, model serving, data pipelines
Experience building or operating autonomous or agentic AI systems

Nice To Haves

Familiarity with quantum computing or quantum error correction
Experience in other AI for Science domains (materials, biology, physics simulation)
Multi-agent system design or orchestration frameworks
Rust or systems programming experience
Reinforcement learning, particularly in combinatorial or physical design spaces
Hardware-adjacent software: FPGA toolchains, embedded control, low-latency inference

Responsibilities

Develop large-scale pretraining, fine tuning and inference efforts for models used in QEC and related classical control problems.
Design, implement, and optimize GPU/CUDA/C++ kernels and distributed training pipelines for high-performance model development.
Collaborate with physicists and engineers to translate quantum phenomena and simulation outputs into ML-relevant data representations.
Prototype and evaluate new ML architectures for solving QEC-related classical control problems.
Work closely with infrastructure teams to ensure seamless integration of ML components into existing quantum computation pipelines.
Drive best practices in ML experimentation, data curation, and reproducible research within the team.

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