ML Systems Engineer - AI Frameworks & Inference

Meta; Menlo Park (CA); On-site; USD 122,000 - 181,000; Full time

Benefits offered by this job

Bonus

Equity

Benefits

Job summary

Meta seeks a Software Engineer in Systems ML - Frameworks to develop and optimize the software stack for AI frameworks, compilers, and accelerators. You will contribute to the PyTorch core and related tooling to support state-of-the-art inference hardware, and you'll analyze models to implement compiler optimizations across DL workloads.

You will accelerate deep learning systems spanning CV, NLP, and generative AI, focusing on performance and scalability while collaborating with cross-functional

Qualifications

• Bachelor's degree or equivalent practical experience in CS/CE or related field.
• Proficient in Python and C/C++ programming.
• Experience in AI framework development or accelerating deep learning models on hardware architectures.
• Must have work authorization to work in the United States at the time of hire and ongoing employment.

Responsibilities

• Develop software stack focusing on AI frameworks, compiler stacks, and high-performance kernel development for AI accelerators.
• Contribute to PyTorch core compilers to support new hardware accelerators and optimize performance.
• Analyze neural networks and implement compiler optimization algorithms.
• Accelerate next-generation DL models including RL, CV, NLP, and generative AI.
• Performance tuning and optimization of deep learning framework and components.

Skills

Python

C/C++

Education

Bachelor's degree in Computer Science, Computer Engineering, or related technical field

Job description

Meta seeks a Software Engineer in Systems ML - Frameworks to develop and optimize the software stack for AI frameworks, compilers, and accelerators. You will contribute to the PyTorch core and related tooling to support state-of-the-art inference hardware, and you'll analyze models to implement compiler optimizations across DL workloads.

You will accelerate deep learning systems spanning CV, NLP, and generative AI, focusing on performance and scalability while collaborating with cross-functional