Software Engineer, Systems ML
About the role
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.
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
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
- 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 codebase