Software Engineer, Systems ML - Compilers / Backend

Meta•Sunnyvale, CA

4d•$154,003 - $217,000

About The Position

Reality Labs (RL) focuses on delivering Meta's vision through Virtual Reality (VR), Augmented Reality (AR) and Wearable AI Devices. The compute performance and power efficiency requirements of our AI devices require custom silicon. Reality Labs Silicon team is driving the state of the art forward with breakthrough work in computer vision, machine learning, mixed reality, graphics, displays, sensors, and new ways to map the human body. Our chips will unlock personalized on-device AI capabilities and blend virtual, physical worlds on wearable devices. We believe the only way to achieve our goals is to look at the entire stack, from transistors, through architecture, firmware, and algorithms. 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.

Requirements

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
2+ years experience developing compilers, toolchains, 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 (GPU, TPU, custom AI ASICs)

Nice To Haves

Experience of developing in a mainstream machine-learning framework, e.g. PyTorch, MLIR, Tensorflow or Caffe
Experience with machine-code generation or compiler back-ends for on-device inference workloads
Experience working and communicating cross functionally in a team environment
Experience working on and contributing to an active compiler toolchain codebase, such as LLVM, MLIR, GCC, MSVC, Glow
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Experience in deep learning algorithms and techniques, e.g., convolutional neural networks, recurrent networks, etc
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Experience developing high-performance kernels or runtime components and tuning them for inference specific accelerator platforms

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
Work with hardware architects to co-design hardware features that maximize performance, power efficiency and programmability
Contribute to the development of machine-learning libraries, intermediate representations, export formats, and analysis tools
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
Conduct design and code reviews.
Evaluate code performance, debug, diagnose and drive resolution of compiler and cross-disciplinary system issues
Interface with other compiler-focused teams to evaluate and incorporate their innovations and vice versa