Our Fundamental AI Research (FAIR) organization is seeking a Research Engineer to drive advancements in generative models. The role involves working across the full spectrum of research, engineering, and optimization for frontier model efforts.
Research Engineer, Fundamental AI Research (FAIR) – Generative Models/LLM Acceleration Responsibilities
Innovate, lead, and execute pioneering algorithmic research to push the state-of-the-art in generative models and LLM performance
Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results
Collaborate with cross-functional teams (research, product, infra) to build new and advance LLM optimization
Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
Lead initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
Take a significant role in components, features, or systems with good end-to-end understanding
Contribute to publications, open-sourcing initiatives, and mentor other team members.
Research Engineer, Fundamental AI Research (FAIR) – Generative Models/LLM Acceleration Responsibilities
Innovate, lead, and execute pioneering algorithmic research to push the state-of-the-art in generative models and LLM performance
Directly contribute to the experimental process, including designing details, implementing reusable code, running evaluations, and organizing results
Collaborate with cross-functional teams (research, product, infra) to build new and advance LLM optimization
Analyze and optimize code for quality, efficiency, and performance, and provide feedback to peers during code reviews
Lead initiatives, provide technical guidance and mentorship to peers, and help onboard new team members
Take a significant role in components, features, or systems with good end-to-end understanding
Contribute to publications, open-sourcing initiatives, and mentor other team members.
Requirements:
Minimum Qualifications
Master's degree or higher in a relevant technical field (e.g., Computer Science, Machine Learning, AI, or related discipline)
6+ years of experience in machine learning, deep learning, or AI research, or equivalent practical experience
Experience designing and implementing large-scale model training pipelines using frameworks such as PyTorch or JAX
Experience with distributed computing and parallel training techniques including data parallelism, model parallelism, or pipeline parallelism
Experience debugging and optimizing AI systems for performance, reliability, and correctness across the full model lifecycle
Preferred Qualifications
Experience building evaluation frameworks and benchmarking pipelines to measure model quality and capability regressions
Experience with large language model pretraining, fine-tuning, post-training, or inference optimization
Track record of contributions to peer-reviewed AI research publications or open-source AI frameworks.
Minimum Qualifications
Master's degree or higher in a relevant technical field (e.g., Computer Science, Machine Learning, AI, or related discipline)
6+ years of experience in machine learning, deep learning, or AI research, or equivalent practical experience
Experience designing and implementing large-scale model training pipelines using frameworks such as PyTorch or JAX
Experience with distributed computing and parallel training techniques including data parallelism, model parallelism, or pipeline parallelism
Experience debugging and optimizing AI systems for performance, reliability, and correctness across the full model lifecycle
Preferred Qualifications
Experience building evaluation frameworks and benchmarking pipelines to measure model quality and capability regressions
Experience with large language model pretraining, fine-tuning, post-training, or inference optimization
Track record of contributions to peer-reviewed AI research publications or open-source AI frameworks.
This position is open to all candidates.
