Research Engineer, SysML - FAIR

Job Title

Research Engineer, Fundamental AI Research (FAIR)

About the Role

Join Meta FAIR and contribute to the advancement of artificial intelligence by making fundamental breakthroughs in AI technologies. This role focuses on solving complex systems challenges to accelerate progress toward human-level intelligence. You will have the opportunity to design and build large-scale ML systems and infrastructures, focusing on usability, efficiency, and sustainability. This position is ideal for individuals passionate about advancing AI through open science innovations.

Responsibilities

• Conduct cutting-edge research to advance the science and technology of machine learning systems.
• Perform research enabling the learning of data semantics across various modalities (images, video, text, audio, etc.).
• Devise improved data-driven models for AI system design and optimization.
• Contribute research leading to innovations in scalable machine learning systems, resource-efficient AI data and algorithm scaling, neural network architectures, and memory/energy-efficient AI systems.
• Develop and implement environmentally-sustainable AI system and hardware designs.
• Collaborate with researchers and cross-functional partners, communicating research plans, progress, and results.
• Publish research findings and contribute to research that impacts Meta product development.

Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
• Master's degree in Computer Science, Computer Engineering, or a related field.
• 4+ years of domain-specific industry experience in systems development, computer architectures, compiler and programming languages, machine learning, and artificial intelligence.
• Proficiency in Python, C++, C, Rust, or other related languages, and experience with the PyTorch framework.
• Experience developing and optimizing systems for large-scale machine learning execution.
• Experience devising data-driven models and conducting real-system experiments for AI system optimization.
• Experience with scalable machine learning systems, resource-efficient AI data and algorithm scaling, or neural network architectures.
• Proven ability to solve complex problems, compare alternative solutions, and evaluate tradeoffs.
• Experience working and communicating cross-functionally in a team environment.
• PhD in Computer Science, Computer Engineering, or a relevant technical field, or equivalent practical experience.
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).
• Demonstrated research and software engineering experience through work experience, coding competitions, or significant open-source contributions (e.g., GitHub).
• Proven track record of significant achievements via grants, fellowships, patents, and publications at leading AI conferences and journals (e.g., MLSys, ISCA, ASPLOS, HPCA, PLDI, CGO, NeurIPS, ICML, ICLR, or similar).
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements).