Software Engineer, Systems ML

Meta is seeking a Software Engineer to join our Systems ML Engineering team, focused on building and optimizing the machine learning infrastructure that powers Meta's products at massive scale. In this role, you will design and develop high-performance ML systems, working across the full stack from model training and inference pipelines to hardware-aware optimizations. You will collaborate with researchers, platform engineers, and product teams to accelerate ML workloads and improve the efficiency of AI infrastructure that serves billions of users.

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience6+ years of experience in software engineering with a focus on machine learning systems, AI infrastructure, or high-performance computing
• Experience developing and optimizing ML training or inference pipelines using frameworks such as PyTorch, Tensor Flow, or equivalent
• Experience with distributed computing architectures and large-scale systems design for ML workloads
• Experience programming in C++ and Python for performance-critical systems
• Experience using profiling and performance analysis tools to identify and resolve bottlenecks in ML or compute-intensive systems
• Experience optimizing large-scale ranking and recommendation model inference on AI accelerator hardware
• Experience with hardware-software co-design, including numerics optimization and SIMD or vectorization techniques
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Experience with GPU programming using CUDA, ROCm, or equivalent hardware accelerator kernel development
• Experience with ML compiler technologies such as MLIR, LLVM, TVM, XLA, or IREE
• 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)