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# Software Engineer, Systems ML - Compilers / Backend

- **Company:** The Meta Game, Inc.
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $154,003.0 - $217,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Augmented Reality, Compilers, Code Generation, Program Optimization, Computer Programming, Computer Engineering, Software Debugging, Visual C++ (Programming Language), Firmware, Python (Programming Language), Machine Learning, Tensorflow, Software Engineering, Toolchain, Application Specific Integrated Circuits, Pytorch, Virtual Reality, Deep Learning, Convolutional Neural Networks, Backend, Information Technology, Hardware Infrastructure, Mixed Reality
- **Published:** August 2, 2026
- **Apply:** https://dejobs.org/x/x/F94DB501D3D943D89629BC2C57660522/job/

## About the Role

Software Engineer, Systems ML - Compilers / Backend Responsibilities, 8. Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

 9. 2+ years experience developing compilers, toolchains, runtime, or similar code optimization software
 10. Experience in software design and programming experience in Python and/or C/C++ for development, debugging, testing and performance analysis
 11. Experience in AI framework development or accelerating models on hardware architectures (GPU, TPU, custom AI ASICs), 12. Experience of developing in a mainstream machine-learning framework, e.g. PyTorch, MLIR, Tensorflow or Caffe
 13. Experience with machine-code generation or compiler back-ends for on-device inference workloads
 14. Experience working and communicating cross functionally in a team environment
 15. Experience working on and contributing to an active compiler toolchain codebase, such as LLVM, MLIR, GCC, MSVC, Glow
 16. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
 17. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
 18. Experience in deep learning algorithms and techniques, e.g., convolutional neural networks, recurrent networks, etc
 19. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
 20. Experience developing high-performance kernels or runtime components and tuning them for inference specific accelerator platforms

## Description

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., 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


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