Principal ML Systems Engineer – AI for Quantum

PsiQuantumsmission is to build the first useful quantum computersmachines 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 approachisbased on silicon photonics. Byleveragingthe advanced semiconductor manufacturing industryincluding partners like GlobalFoundrieswe use the same high-volume processes that already produce billions of chips for telecom and consumer electronics. Photonics offers natural advantages for scale: photonsdontfeel heat are immune to electromagnetic interference and integrate with existing cryogenic cooling and standard fiber-optic infrastructure.

In 2024PsiQuantumannounced 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 definingand that now is the time to scale.

PsiQuantumalso develops the algorithms and software needed to make these systems commercially valuable. Our application software and industry teams work directly with leading Fortune 500 companiesincluding Lockheed Martin Mercedes-Benz Boehringer Ingelheim and Mitsubishi Chemicalto prepare quantum solutions for real-world impact.

Quantum computing is not an extension of classical computing. Itrepresentsa fundamental shiftand a path to mastering challenges that cannot besolvedany other way. The potential is enormous and we have a clearpathto 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 dont exist yet.

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.

Experience/Qualifications

Required skills and experiences:

• 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

PsiQuantum provides equal employment opportunity for all applicants and employees. PsiQuantum does not unlawfully discriminate on the basis of race color religion sex (including pregnancy childbirth or related medical conditions) gender identity gender expression national origin ancestry citizenship age physical or mental disability military or veteran status marital status domestic partner status sexual orientation genetic information or any other basis protected by applicable laws.

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