Software Engineer, Systems ML - Compilers

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.

Software Engineer, Systems ML - Compilers Responsibilities:

Lead the architecture and implementation of ML compiler infrastructure, including intermediate representations (IR), optimization passes, and code generation targeting custom AI accelerators

Design and implement compiler transformations informed by hardware architecture constraints for GPU, TPU, and edge AI accelerators

Drive the development of LLVM/MLIR-based tool chains for compiling PyTorch models to optimized binaries for custom silicon

Work with hardware architects to co-design compiler features that maximize performance, power efficiency, and programmability for edge devices

Analyze and improve the efficiency, scalability, and stability of compiler tool chains, ensuring they can be extended to new hardware targets

Lead technical road mapping for compiler infrastructure initiatives, coordinate execution across teams, and mentor engineers on compiler design patterns

Conduct design and code reviews, evaluate code performance, and drive resolution of compiler and cross-disciplinary system issues

Interface with other compiler-focused teams (PyTorch, Execu Torch) to evaluate and incorporate innovations

Minimum Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience3+ years of experience in developing compilers, tool chains, or code optimization software

Experience with LLVM, MLIR, or similar compiler infrastructure frameworks

Experience in designing intermediate representations and implementing compiler optimization passes

Experience with hardware architectures such as GPUs, TPUs, or custom AI accelerators

Experience in software development using C++ for compiler and systems-level programming

Experience leading end-to-end technical design and delivery of compiler infrastructure initiatives across multiple teams

Preferred Qualifications:

Experience developing in ML frameworks such as PyTorch or Tensor Flow at the system level

Experience co-designing software and hardware features with silicon architecture teams

Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

Experience with Execu Torch, TensorRT, XLA, or similar ML compilation and deployment frameworks

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Experience with power and performance optimization for resource-constrained edge devices

Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

Experience with machine-code generation or compiler back-ends targeting edge or on-device inference workloads

About Meta:

Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and Whats App further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building…