Lead the evolution of our high-performance robotics simulation platform
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Design and implement the compute infrastructure and data flow mechanisms to optimize performance for physics simulation and foundation model training
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Lead development of our compiler stack, focusing on JIT compilation, LLVM IR, and GPU codegen to minimize compile time and maximize runtime performance
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Collaborate with the team to improve the compiler’s support for differentiable programming, crucial for training neural networks within simulations
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Stay current on state-of-the-art ML compilers—such as those in torch, Triton, and JAX—and decide which techniques and approaches are best suited for our application
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Work closely with simulation and robotics engineers to align compiler enhancements with application needs
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Contribute to relevant open-source projects and participate actively in the broader compiler and systems community
What You’ll Bring
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Strong background in compiler construction, particularly in JIT compilation and LLVM-based code generation
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Extensive experience with GPU programming models (e.g., CUDA, Vulkan) and understanding of GPU architecture
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Track record as a core contributor to GPU programming infrastructure—such as Torch, JAX, Mojo, Taichi, or Warp
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Proven ability to profile and optimize complex systems for performance and scalability
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Understanding of automatic differentiation and its application in simulation and machine learning contexts
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Excellent communication skills and a collaborative approach to problem-solving
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Enthusiasm for contributing to and engaging with open-source communities