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Salary: CAD 140,000 - 190,000 / annual
Xanadu is building quantum computers that are useful and available globally. As an AI Specialist, you will drive applied AI initiatives by analyzing diverse R&D data and processes using state-of-the-art machine learning techniques including representation learning, generative modeling, and reinforcement learning. You will uncover hidden patterns in research across technical fields and contribute directly to developing internal R&D tool stacks that advance the first commercially viable quantum computer.
Key responsibilities include investigating and analyzing complex structured and unstructured data from internal R&D projects to identify trends and insights; developing generalizable representation, reinforcement, and generative learning strategies for diverse research data; designing and implementing machine learning and optimization algorithms based on learned representations; developing and rigorously testing new ML algorithms and toolkits to improve R&D efficiency; and collaborating closely with hardware engineers and scientists to create novel ML-driven solutions for complex research challenges.
You will establish and maintain reproducible data analysis and modeling workflows. The tech stack includes Python, Jupyter, JAX, GitHub, Docker, CI pipelines, and multiple cloud platforms.
Required qualifications: BSc in Physics, Math, Computer Science, Engineering, or related field; 4+ years of industry experience in deep learning/AI/ML with expertise in at least one of: representation learning, reinforcement learning, geometric deep learning, computer vision, NLP, generative models, GFlowNet, or control theory; strong Python proficiency with numerical/scientific libraries (JAX, NumPy, pandas, xarray, PyTorch, CUDA, SciPy, scikit-learn, Ray); deep mathematical understanding of machine learning and optimization; hands-on experience designing generalizable representations of complex data structures with symmetries; experience with large-scale neural network training for RL, LLMs, diffusion models, or generative modeling; software development lifecycle experience including version control, code review, testing, CI/CD, logging, profiling, debugging, and documentation; comfort with Linux shell, Docker, Git, and GitHub; enthusiasm for learning new technologies with minimal supervision; solid communication and collaboration skills; strong analytical and problem-solving abilities; and good knowledge of physics and linear algebra.
Preferred: MSc/PhD in Computer Science, Engineering, Physics, Math, or related field; excellent physics and linear algebra knowledge; familiarity with quantum computing; experience with GFlowNet, geometric deep learning and equivariant models, ML on ultrafast embedded systems, or modeling/simulation of physical systems on HPC hardware; or experience training commercial-grade LLMs.
