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Meta · 2 days ago

AI Research Scientist - Safety Alignment Team

Menlo Park, CA

Full-time

Onsite

Mid, Senior Level

$184K/yr - $257K/yr

3+ years exp

Meta is seeking AI Research Scientists to join the Safety Alignment team within Meta Superintelligence Labs, dedicated to advancing the safe development and deployment of superintelligent AI. The role involves designing and evaluating safety alignment techniques, creating and analyzing datasets, and leading technical projects to integrate safety alignment into Meta's products and services.

Computer Software

Comp. & Benefits

Responsibilities

Design, implement, and evaluate novel safety alignment techniques for large language models and multimodal AI systems

Create, curate, and analyze high-quality datasets for safety alignment

Fine-tune and evaluate LLMs to adhere to Meta’s safety policies and evolving global standards

Build scalable infrastructure and tools for safety evaluation, monitoring, and rapid mitigation of emerging risks

Work closely with researchers, engineers, and cross-functional partners to integrate safety alignment into Meta’s products and services

Lead complex technical projects end-to-end

Qualification

LLM/NLP experiencePython programmingMachine LearningTechnical project leadershipPublications in conferencesRL techniquesSafety alignment innovationSafety dataset evaluationDistributed trainingBachelor's degreePhD in relevant field

Required

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

3+ years of industry research experience in LLM/NLP, computer vision, or related AI/ML model training

Experience as a technical lead on a team and/or leading complex technical projects from end-to-end

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

Preferred

Hands-on experience applying RL techniques (e.g., RLHF, PPO, DPO, GRPO, RLVF, reward modeling) to fine-tune large language models for safety and policy adherence

Experience developing, fine-tuning, or evaluating LLMs across multiple languages and modalities (text, image, voice, video)

Demonstrated experience to innovate in safety alignment, 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 pairs for risk mitigation

Experience with distributed training of LLMs (hundreds/thousands of GPUs), scalable safety mitigations, and automation of safety tooling