Your role and responsibilities:
As an AI Specialist at Xanadu, you will drive applied AI initiatives by deeply analyzing diverse R&D data and processes. Using state-of-the-art machine learning and AI techniques, such as representation learning, generative modeling, reinforcement learning etc., you will uncover hidden patterns in research across various technical fields. This work will directly contribute to developing internal R&D tool stacks to advance 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 AI/ML to enhance software tools and potentially transform research processes. You will:
- Investigate and analyze complex structured and unstructured data from various internal R&D projects to identify key trends and insights.
- Develop generalizable representation/reinforcement/generative learning strategies for diverse research data, addressing both theoretical and engineering challenges.
- Design and implement machine learning and optimization algorithms based on learned representations to solve specific R&D problems.
- 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, Jupyter, Jax, GitHub, Docker, CI pipelines, 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.
- 4+ years of industry experience in deep learning/AI/ML, including at least one of these topics: representation learning, reinforcement learning, geometric deep learning, computer vision, NLP, generative models, GFlowNet, control theory.
- Strong knowledge of Python and its numerical/scientific ecosystem (jax, numpy, pandas, xarray, pytorch, cuda, scipy, sklearn, ray, etc.)
- Deep mathematical understanding of machine learning and optimization
- Experience with designing and building novel and generalizable representations of complex data structures with symmetries.
- Hands-on experience with large scale training of neural networks for RL, LLMs, diffusion models, or other types of generative modeling.
- 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 and linear algebra.
Preferred qualifications and experience:
- MSc/PhD in Computer Science, Engineering, Physics, Math, or related field.
- Excellent knowledge in physics and linear algebra.
- Familiarity with or curiosity towards quantum computing.
- Rich experience in any of these areas:
- GFlowNet
- Geometric deep learning and equivariant models.
- ML on ultrafast embedded systems
- Modeling and simulation of physical systems on high performance computing hardware
- Training of commercial grade LLMs
This is for a new position. Your base salary will be determined based on your location, experience, and internal benchmarks. The base salary range is 140,000 - 190,000 CAD. You will also be eligible for equity and benefits.
