Software Engineer, Systems ML

Meta; Baton Rouge (LA); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta seeks a Software Engineer for its Systems ML Engineering team to design and build high-performance ML infrastructure across training and inference pipelines. You will optimize ML systems with hardware-aware techniques and collaborate with researchers, platform engineers, and product teams to accelerate AI workloads serving billions of users.

The role requires strong C++ and Python, distributed systems experience, and a track record of profiling and optimizing performance at scale.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or equivalent practical experience
• 6+ years of software engineering experience focused on ML systems, AI infrastructure, or HPC
• Experience with ML training/inference pipelines using PyTorch, TensorFlow, or equivalents
• Experience with distributed computing architectures for ML workloads
• Proficiency in C++ and Python for performance-critical systems
• Experience with profiling/performance analysis to identify bottlenecks

Responsibilities

• Design, build, and optimize large-scale ML training and inference systems
• Develop and maintain high-performance ML infrastructure components in C++ and Python
• Identify and resolve performance bottlenecks across the ML stack
• Architect and evaluate trade-offs in ML system design (memory bandwidth, compute, I/O)
• Partner with research/product teams to translate ML requirements into infrastructure solutions
• Define and track system-level metrics and reliability of ML serving systems
• Lead technical design reviews and engineering standards for ML systems
• Mentor engineers on ML infrastructure and performance optimization
• Drive adoption of AI-augmented development workflows
• Contribute to staged rollout strategies using feature flags and experimentation

Job description

Summary:

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.

Required Skills:

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

Minimum Qualifications:

1. Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience

2. 6+ years of experience in software engineering with a focus on machine learning systems, AI infrastructure, or high-performance computing

3. Experience developing and optimizing ML training or inference pipelines using frameworks such as PyTorch, TensorFlow, or equivalent

4. Experience with distributed computing architectures and large-scale systems design for ML workloads

5. Experience programming in C++ and Python for performance-critical systems

6. Experience using profiling and performance analysis tools to identify and resolve bottlenecks in ML or compute-intensive systems

Preferred Qualifications:

1. Experience optimizing large-scale ranking and recommendation model inference on AI accelerator hardware

2. Experience with hardware-software co-design, including numerics optimization and SIMD or vectorization techniques

3. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)

4. Experience with GPU programming using CUDA, ROCm, or equivalent hardware accelerator kernel development

5. Experience with ML compiler technologies such as MLIR, LLVM, TVM, XLA, or IREE

6. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies

7. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)

Public Compensation:

$154,003/year to $217,000/year + bonus + equity + benefits

Industry:

Internet

Equal Opportunity:

Meta is proud to be an Equal Employment Opportunity and Aff…

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