Key skills
PythonAIMachine LearningMLNLPComputer VisionLLMLarge Language ModelsPyTorch
About this role
Meta is seeking Research Engineers to join the Multimodal Embodiment Trust team within Meta Superintelligence Labs, dedicated to advancing the safe development and deployment of Superintelligent AI. Product & Applied Research group is focused on building AI-powered experiences for people, bringing frontier models to consumers. Our two primary goals are: to build a superintelligent personal sidekick that billions of people use to make their lives better; and to provide fresh, personal, insightful entertainment by allowing people to make, share, and consume AI-generated media and immersive experiences.
Responsibilities
Design, implement, and evaluate novel, systemic, and foundational safety techniques for large language models and multimodal AI systems
* Create, curate, and analyze high-quality datasets for safety system and foundations
* Fine-tune and evaluate LLMs to adhere to Meta’s safety policies and evolving global standards
* Contribute to applied research through risk analysis, experimentation, measurement, and and building mitigations
* Work closely with researchers, engineers, and cross-functional partners to integrate safety solutions into Meta’s products and services
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
* PhD in Computer Science, Machine Learning, or a relevant technical field
* Experience in LLM/NLP, computer vision, or related AI/ML model training
* End-to-end experience working on complex technical projects
* Publications at peer-reviewed conferences (e.g. ICLR, NeurIPS, ICML, KDD, CVPR, ICCV, ACL)
* Programming experience in Python and hands-on experience with frameworks such as PyTorch Hands-on experience applying state-of-the-art techniques to build robust AI system solutions for safety and policy adherence
* Experience developing, fine-tuning, or evaluating LLMs across multiple languages and modalities (text, image, voice, video, reasoning, etc)
* Demonstrated experience to innovate in safety foundational research, including custom guideline enforcement, dynamic policy adaptation, and rapid hotfixing of model vulnerabilities
* Experience designing, curating, and evaluating safety datasets, including adversarial and borderline prompt cases
* Experience with distributed training of LLMs (hundreds/thousands of GPUs), scalable safety mitigations, and automation of safety tooling
Responsibilities
Design, implement, and evaluate novel, systemic, and foundational safety techniques for large language models and multimodal AI systems
* Create, curate, and analyze high-quality datasets for safety system and foundations
* Fine-tune and evaluate LLMs to adhere to Meta’s safety policies and evolving global standards
* Contribute to applied research through risk analysis, experimentation, measurement, and and building mitigations
* Work closely with researchers, engineers, and cross-functional partners to integrate safety solutions into Meta’s products and services
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
* PhD in Computer Science, Machine Learning, or a relevant technical field
* Experience in LLM/NLP, computer vision, or related AI/ML model training
* End-to-end experience working on complex technical projects
* Publications at peer-reviewed conferences (e.g. ICLR, NeurIPS, ICML, KDD, CVPR, ICCV, ACL)
* Programming experience in Python and hands-on experience with frameworks such as PyTorch Hands-on experience applying state-of-the-art techniques to build robust AI system solutions for safety and policy adherence
* Experience developing, fine-tuning, or evaluating LLMs across multiple languages and modalities (text, image, voice, video, reasoning, etc)
* Demonstrated experience to innovate in safety foundational research, including custom guideline enforcement, dynamic policy adaptation, and rapid hotfixing of model vulnerabilities
* Experience designing, curating, and evaluating safety datasets, including adversarial and borderline prompt cases
* Experience with distributed training of LLMs (hundreds/thousands of GPUs), scalable safety mitigations, and automation of safety tooling