About the job
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.
Responsibilities
Analyze and design effective compiler passes and optimizations. Implement and/or enhance code generation targeting machine learning accelerators
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
Contribute to the development of machine-learning libraries, intermediate representations, export formats, and analysis tools
Conduct design and code reviews. Evaluate code performance, debug, diagnose and drive resolution of compiler and cross-disciplinary system issues
Analyze and improve the efficiency, scalability, and stability of our toolchains
Interface with other compiler-focused teams to evaluate and incorporate their innovations and vice versa
Qualifications
Minimum
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
2+ years experience developing compilers, runtime, or similar code optimization software
Experience in software design and programming experience in Python and/or C/C++ for development, debugging, testing and performance analysis
Experience in AI framework development or accelerating models on hardware architectures
Preferred
Experience working and communicating cross functionally in a team environment.
Experience with machine-code generation or compiler back-ends.
Experience working on and contributing to an active compiler toolchain codebase, such as LLVM, MLIR, GCC, MSVC, Glow.
Experience in deep learning algorithms and techniques, e.g., convolutional neural networks, recurrent networks, etc.
Experience of developing in a mainstream machine-learning framework, e.g. PyTorch, MLIR, Tensorflow or Caffe.
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies