Senior Software Engineer, AI for Quantum

PsiQuantum • Palo Alto, CA

50d

About The Position

PsiQuantum is seeking an experienced Machine Learning (ML) expert to join our AI for Quantum initiative, focused on accelerating progress in Quantum Error Correction (QEC) and related computational research. This role will partner closely with physicists and software engineers to develop, optimize, and scale ML models supporting quantum error correction and error modelling. The ideal candidate brings deep expertise in large-scale model training and systems optimization, a solid mathematical or physical sciences foundation, and the ability to integrate advanced ML methods into a broader scientific software stack.

Requirements

• 8+ years of professional experience with a strong background in mathematics or physics (Bachelor’s or higher in a quantitative field such as Physics, Applied Math, Computer Science, or related discipline).
• Proven expertise in large-scale pretraining, distributed optimization, and scaling methods (e.g., model/data parallelism).
• Fluent in Python and at least one systems programming language (C, C++, Rust).
• Proficiency in GPU programming and kernels (e.g. CUDA, Triton, Warp, CuTe, CUTLASS).
• Experience with GPU programming and performance profiling for ML workloads.
• Familiarity with modern ML frameworks such as PyTorch and JAX.
• Excellent communication skills and ability to collaborate with cross-disciplinary teams spanning physics, ML, and software engineering.
• Excellent communication, problem‑solving, and cross‑team collaboration skills.

Nice To Haves

• Knowledge of model compression, distributed systems, or compiler-level optimization for ML runtimes.
• Proven ability to work closely with subject matter experts, data analysts, or other non-traditional software stakeholders.
• Preference for those with prior exposure to quantum computing concepts or physics-informed ML.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 501-1,000 employees