Software Engineer, Systems ML

Skip to main content Software Engineer, Systems ML Meta Menlo Park, CA Apply Join or sign in to find your next job Join to apply for the Software Engineer, Systems ML role at Meta Email or phone Password Show Forgot password? Sign in Sign in with Email or New to LinkedIn? Join now By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Software Engineer, Systems ML Meta Menlo Park, CA 10 hours ago Be among the first 25 applicants See who Meta has hired for this role Apply Join or sign in to find your next job Join to apply for the Software Engineer, Systems ML role at Meta Email or phone Password Show Forgot password? Sign in Sign in with Email or New to LinkedIn? Join now By clicking Continue to join or sign in, you agree to LinkedIn’s User Agreement, Privacy Policy, and Cookie Policy. Save Report this job Meta provided pay range This range is provided by Meta. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more. Base pay range $154,003.00/yr - $217,000.00/yr 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.Software Engineer, Systems ML Responsibilities:Design, build, and optimize large-scale ML training and inference systems, including distributed computing frameworks and hardware-accelerated pipelinesDevelop and maintain high-performance ML infrastructure components in C++ and Python, ensuring reliability, scalability, and low-latency executionIdentify and resolve performance bottlenecks across the ML stack using profiling, instrumentation, and benchmarking toolsArchitect and evaluate trade-offs in ML system design, including memory bandwidth, compute utilization, and I/O throughputPartner with research and product teams to translate ML model requirements into efficient infrastructure solutionsDefine and track system-level metrics and service level objectives to maintain production reliability of ML serving systemsLead technical design reviews and contribute to engineering standards for ML systems across the organizationMentor other engineers on ML infrastructure best practices, debugging methodologies, and performance optimization techniquesDrive...