Software Engineer, Systems ML

Meta is hiring a Software Engineer for the Systems ML Engineering team in Menlo Park, CA (onsite). In this role, you will help design and optimize large-scale machine learning infrastructure, spanning both model training and inference pipelines. Work centers on hardware-aware performance improvements and reliable production support for ML systems.

What you’ll do

• 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 with an emphasis on reliability, scalability, and low-latency execution
• Use profiling, instrumentation, and benchmarking tools to identify and address performance bottlenecks across the ML stack
• Architect and evaluate trade-offs in ML system design, including memory bandwidth, compute utilization, and I/O throughput
• Collaborate with research and product teams to translate ML model requirements into efficient infrastructure solutions
• Create automated tests for expected behaviors, failure modes, and error paths, and build monitoring and alerting for production anomalies
• Support safe deployment of ML system changes through staged rollout strategies using feature flagging and experimentation frameworks
• Produce accurate technical documentation when introducing new 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 and contribute to engineering programs that improve the health and quality of the ML infrastructure codebase

Minimum qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or a relevant technical field (completed prior to joining Meta), or equivalent practical experience
• 2+ years of experience in software engineering with a focus on machine learning systems, AI infrastructure, or high-performance computing
• Experience developing or optimizing ML training or inference pipelines using PyTorch, TensorFlow, or equivalent frameworks
• 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

Technologies

• C++
• Python
• PyTorch
• TensorFlow

Preferred qualifications

• Demonstrated ongoing AI skill development (for example, prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Demonstrated ability to integrate AI tools to optimize or redesign workflows and drive measurable impact such as efficiency gains or quality improvements
• Experience with GPU programming using CUDA, ROCm, or equivalent hardware accelerator kernel development
• Experience adhering to and implementing responsible, ethical AI practices, including risk assessment, bias mitigation, and quality and accuracy reviews
• Experience with hardware-software co-design, including numerics optimization and SIMD or vectorization techniques
• Experience optimizing large-scale ranking or recommendation model inference on AI accelerator hardware such as GPUs or TPUs

Compensation: USD 121,992 - 181,000 per year.

Location: Menlo Park, CA (onsite).