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# Software Engineer, Systems ML

- **Company:** The Meta Game, Inc.
- **Location:** Menlo Park, CA, United States
- **Experience:** Expert
- **Salary:** $154,003.0 - $217,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Profiling, Nvidia CUDA, Computer Engineering, Software Debugging, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Linux Kernel, Machine Learning, Performance Tuning, Tensorflow, Software Engineering, AI Infrastructure, High Performance Computing, Pytorch, Delivery Pipeline, Gpu Programming, Information Technology, Low Latency, Machine Learning Operations
- **Published:** July 8, 2026
- **Apply:** https://dejobs.org/x/x/82C309EFBAC14A89B5F881F17EEB9FE2/job/

## About the Role

11. Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
 12. 6+ years of experience in software engineering with a focus on machine learning systems, AI infrastructure, or high-performance computing
 13. Experience developing and optimizing ML training or inference pipelines using frameworks such as PyTorch, TensorFlow, or equivalent
 14. Experience with distributed computing architectures and large-scale systems design for ML workloads
 15. Experience programming in C++ and Python for performance-critical systems
 16. Experience using profiling and performance analysis tools to identify and resolve bottlenecks in ML or compute-intensive systems, 17. Experience optimizing large-scale ranking and recommendation model inference on AI accelerator hardware
 18. Experience with hardware-software co-design, including numerics optimization and SIMD or vectorization techniques
 19. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
 20. Experience with GPU programming using CUDA, ROCm, or equivalent hardware accelerator kernel development
 21. Experience with ML compiler technologies such as MLIR, LLVM, TVM, XLA, or IREE
 22. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
 23. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

## Description

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., 1. Design, build, and optimize large-scale ML training and inference systems, including distributed computing frameworks and hardware-accelerated pipelines

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


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