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 Write automated tests covering expected behaviors, failure modes, and error paths for ML infrastructure components, and build monitoring and alerting for production anomalies Contribute to staged rollout strategies using feature flagging and experimentation frameworks to safely deploy ML system changes Produce accurate technical documentation when introducing new ML infrastructure features or updating existing systems Participate in on-call rotations to investigate and mitigate production incidents affecting ML serving systems, and contribute detailed retrospectives Provide constructive code review feedback to other engineers and contribute to engineering programs that improve the health and quality of the ML infrastructure codebaseCurrently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. 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.