Develop MLIR-based graph representations and optimizations for future GPU architectures. Partner with framework and hardware teams to enable new model patterns and upcoming GPU architectural features. Define APIs and MLIR dialects, conduct performance optimizations and analysis, implement compiler optimizations and kernel generation for neural networks, and contribute to other general software engineering work. What we need to see:Bachelor's, Master's, or PhD. in Computer Science, Computer Engineering, a related field, or equivalent experience . years of relevant work or research experience in performance analysis and compiler optimizations. Experience with compiler technologies such as MLIR, XLA, and LLVM. Excellent C/ C and Python programming and software design skills, including debugging, performance analysis, and testing. Ability to work independently, define project goals and scope, and lead your own development efforts. Strong interpersonal skills and the ability to thrive in a fast-moving, dynamic, product-oriented team. Ways to stand out from the crowd:Understanding of deep learning models, algorithms, and frameworks such as Py. Torch and JAX. Experience with GPU kernel generation targeting high performance and fast build times. Proficiency in GPU architecture with CUDA or Open. CL programming experience. A track record of mentoring early career engineers and interns is a bonus. With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our exclusive engineering teams are rapidly growing. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you.