AI Specialist - Simulation & Optimization

About Xanadu: 
Xanadu’s mission is to build quantum computers that are useful and available to people everywhere.
At Xanadu, we are learners, innovators, researchers, collaborators and problem solvers. We are creating something that has never been built before.  What we are doing is extremely hard, the classic moon shot. Few people in their life will be able to be a part of something like this, where if we are successful, the technologies we develop will solve some of the world’s most challenging problems and literally change the world. And that is something to be excited about!
Your role and responsibilities:
As an AI Specialist at Xanadu, you will drive applied AI initiatives by optimizing the very core of our R&D efforts. Your work will focus on enhancing the performance of our most crucial systems, from the design and characterization of our quantum hardware to the execution of internal simulation software on high-performance computing platforms. Using state-of-the-art machine learning, GPU programming, and advanced optimization techniques, you will accelerate our progress toward the first commercially viable quantum computer. The AI team focuses on building and improving modeling, optimization, simulation, data processing, and design methodology for all internal research. At the intersection of multiple technical disciplines, you will collaborate with leading researchers, scientists, engineers, and software developers, using cutting-edge ML to enhance software tools and potentially transform research processes. You will:

• Develop and implement sophisticated, ML-based optimization strategies to tune the parameters of simulated systems and physical hardware, combined with statistical analysis of output uncertainties.
• Optimize the execution of internal simulation software by leveraging GPU programming, code profiling & optimization, and high-performance computing (HPC) platforms.
• Design and build high-performance simulators for complex physical systems, often modeled by differential equations.
• Develop and rigorously test new ML algorithms and tool kits to improve R&D efficiency.
• Collaborate closely with hardware engineers and scientists to create and implement novel ML-driven solutions for complex research challenges.
• Establish and maintain reproducible data analysis and modeling workflows.

At Xanadu, we primarily work with Python, Jax, GitHub, Docker, CI pipelines, Jupyter and multiple cloud platforms. Proficiency in these technologies is essential.

Basic qualifications and experience:

• BSc. in Physics, Math, Computer Science, Engineering, or a related field.
• 6+ years of industry experience in any of the fields: ML/AI, physical sciences, physical design and optimization, simulation software, data science, statistics, or control systems, etc.
• Strong knowledge of Python and its numerical/scientific ecosystem (numpy, pandas, xarray, pytorch, jax, cuda, scipy, sklearn, ray, etc.)
• Hands-on experience with ML-based optimization and the optimization of numerics or simulation software.
• Experience writing and designing simulators for complex physical systems (e.g., differential equations) and control systems.
• Experience with GPU programming (e.g., CUDA).
• Experience with software development lifecycles, including version control, code review, testing, CI/CD, logging, profiling, debugging, and documentation.
• Comfortable working with Linux shell, Docker, Git, and GitHub.
• Enthusiasm for learning new technologies and scientific concepts with minimal supervision.
• Solid communication and collaboration skills.
• Strong self-driven analytical and problem-solving abilities.
• Good knowledge of physics, linear algebra, and statistics.

Preferred qualifications and experience:

• MSc/PhD in Computer Science, Engineering, Physics, Math, or related field.
• Excellent knowledge in physics and linear algebra.
• Experience with representation learning, reinforcement learning, and generative modeling.
• Familiarity with or curiosity towards quantum computing.
• Rich experience in any of these areas:
  • Statistical inference
  • GPU programming
  • ML or signal analysis on ultrafast embedded systems
  • Reinforcement learning