Software Engineer, Systems ML - Compilers / Backend

We are seeking a software engineer to support the development of the compiler tool-chain for state-of-the-art deep learning hardware components optimized for AR/VR systems. You will be part of our efforts to architect, design and implement a clean slate compiler for this activity and will be part of a team that includes compiler, machine learning algorithms and software, firmware and ASIC experts. You will contribute to a full stack development effort compiling PyTorch models down to binaries for custom hardware accelerator blocks., 1. Analyze and design effective compiler passes and optimizations. Implement and/or enhance code generation targeting machine learning accelerators

2. Work with algorithm research teams to map ML graphs to hardware implementations, model data-flows, create cost-benefit analysis and estimate silicon power and performance
3. Work with hardware architects to co-design hardware features that maximize performance, power efficiency and programmability
4. Contribute to the development of machine-learning libraries, intermediate representations, export formats, and analysis tools
5. Analyze and improve the efficiency, scalability, and stability of our toolchains. Optimize and tune kernels and compiled code to achieve latency targets for ML inference
6. Conduct design and code reviews. Evaluate code performance, debug, diagnose and drive resolution of compiler and cross-disciplinary system issues
7. Interface with other compiler-focused teams to evaluate and incorporate their innovations and vice versa

1. Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
2. 2+ years experience developing compilers, toolchains, runtime, or similar code optimization software
3. Experience in software design and programming experience in Python and/or C/C++ for development, debugging, testing and performance analysis
4. Experience in AI framework development or accelerating models on hardware architectures (GPU, TPU, custom AI ASICs), 1. Experience working and communicating cross functionally in a team environment
5. Experience with machine-code generation or compiler back-ends for on-device inference workloads
6. Experience working on and contributing to an active compiler toolchain codebase, such as LLVM, MLIR, GCC, MSVC, Glow
7. Experience in deep learning algorithms and techniques, e.g., convolutional neural networks, recurrent networks, etc
8. Experience developing high-performance kernels or runtime components and tuning them for inference specific accelerator platforms
9. Experience of developing in a mainstream machine-learning framework, e.g. PyTorch, MLIR, Tensorflow or Caffe