Software Engineer, Systems ML

Meta; Santa Fe (NM); On-site; USD 154,000 - 217,000; Full time

Job summary

Meta is seeking a Software Engineer to join the Systems ML Engineering team in Santa Fe to build and optimize ML infrastructure at massive scale. You will work across the full stack from model training and inference pipelines to hardware-aware optimizations, collaborating with researchers and product teams to accelerate workloads and improve AI infrastructure efficiency.

The role emphasizes high-performance systems, distributed computing, and mentorship of engineers, with an emphasis on

Qualifications

• Experience building and optimizing ML training/inference pipelines.
• Proficiency in C++ and Python for performance-critical systems.
• Experience with distributed ML workloads and large-scale systems.
• Strong profiling/benchmarking to identify bottlenecks.
• Collaborates with researchers and product teams to ship ML infrastructure.
• Comfort with architecture trade-offs and system metrics.

Responsibilities

• Design and optimize large-scale ML training and inference systems.
• Develop high-performance ML infrastructure components.
• Identify and fix performance bottlenecks across the ML stack.
• Collaborate with researchers and product teams on requirements.
• Lead design reviews and set ML system engineering standards.
• Mentor engineers in performance optimization techniques.

Skills

Large-scale ML systems design

C++ and Python performance

Profiling and optimization

ML infrastructure development

Distributed computing

Hardware-aware optimization

Team collaboration

Mentoring engineers

AI-enabled workflows

Feature flag rollout

Education

Bachelor's degree in Computer Science/Engineering or equivalent

Tools

CUDA/ROCm

Profiling tools

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. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
6. 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 $217,006/year + bonus + equity + benefits

Industry:

Internet

Equal Opportunity:

Meta is proud to be an Equal Employment Opportunity and Affi…?

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