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Genesis Aivia Ashby
Member of Technical Staff, ML Compiler and Systems (Paris, London)
ParisPosted 1w ago
ML EngineerStaff+Full-time
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About the Role
What You'll Do
- Lead the evolution of our high-performance robotics simulation platform
- Design and implement the compute infrastructure and data flow mechanisms to optimize performance for physics simulation and foundation model training
- Lead development of our compiler stack, focusing on JIT compilation, LLVM IR, and GPU codegen to minimize compile time and maximize runtime performance
- Collaborate with the team to improve the compiler's support for differentiable programming, crucial for training neural networks within simulations
- 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
- Work closely with simulation and robotics engineers to align compiler enhancements with application needs
- Contribute to relevant open-source projects and participate actively in the broader compiler and systems community
What You’ll Bring
- Strong background in compiler construction, particularly in JIT compilation and LLVM-based code generation
- Extensive experience with GPU programming models (e.g., CUDA, Vulkan) and understanding of GPU architecture
- Track record as a core contributor to GPU programming infrastructure—such as Torch, JAX, Mojo, Taichi, or Warp
- Proven ability to profile and optimize complex systems for performance and scalability
- Understanding of automatic differentiation and its application in simulation and machine learning contexts
- Excellent communication skills and a collaborative approach to problem-solving
- Enthusiasm for contributing to and engaging with open-source communities
- Lead the evolution of our high-performance robotics simulation platform
- Design and implement the compute infrastructure and data flow mechanisms to optimize performance for physics simulation and foundation model training
- Lead development of our compiler stack, focusing on JIT compilation, LLVM IR, and GPU codegen to minimize compile time and maximize runtime performance
- Collaborate with the team to improve the compiler's support for differentiable programming, crucial for training neural networks within simulations
- 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
- Work closely with simulation and robotics engineers to align compiler enhancements with application needs
- Contribute to relevant open-source projects and participate actively in the broader compiler and systems community
What You’ll Bring
- Strong background in compiler construction, particularly in JIT compilation and LLVM-based code generation
- Extensive experience with GPU programming models (e.g., CUDA, Vulkan) and understanding of GPU architecture
- Track record as a core contributor to GPU programming infrastructure—such as Torch, JAX, Mojo, Taichi, or Warp
- Proven ability to profile and optimize complex systems for performance and scalability
- Understanding of automatic differentiation and its application in simulation and machine learning contexts
- Excellent communication skills and a collaborative approach to problem-solving
- Enthusiasm for contributing to and engaging with open-source communities
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