Software Engineer, Systems ML

Meta builds technologies that help people connect, find communities, and grow businesses. Meta is seeking a Software Engineer to design, build, and optimize large-scale machine learning training and inference systems. The role focuses on high-performance ML infrastructure, hardware-aware optimization, system reliability, technical leadership, and collaboration with research and product teams.

Responsibilities:

• Design, build, and optimize large-scale ML training and inference systems, including distributed computing frameworks and hardware-accelerated pipelines
• Develop and maintain high-performance ML infrastructure components in C++ and Python, ensuring reliability, scalability, and low-latency execution
• Identify and resolve performance bottlenecks across the ML stack using profiling, instrumentation, and benchmarking tools
• Architect and evaluate trade-offs in ML system design, including memory bandwidth, compute utilization, and I/O throughput
• Partner with research and product teams to translate ML model requirements into efficient infrastructure solutions
• Define and track system-level metrics and service level objectives to maintain production reliability of ML serving systems
• Lead technical design reviews and contribute to engineering standards for ML systems across the organization
• Mentor other engineers on ML infrastructure best practices, debugging methodologies, and performance optimization techniques
• Drive adoption of AI-augmented development workflows to expand engineering productivity and broaden the scope of deliverables
• Contribute to staged rollout strategies using feature flagging and experimentation frameworks to safely deploy ML system changes

Requirements:

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
• 6+ 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, TensorFlow, 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)