Apply on Employer Site

Meta · 13 hours ago

Research Engineer - Safety System, Evaluation and Foundation

Menlo Park, CA

Full-time

Onsite

Mid, Senior Level

3+ years exp

Meta is seeking Research Engineers to join the Safety System and Foundations team within Meta Superintelligence Labs, dedicated to advancing the safe development and deployment of Superintelligent AI. The role involves designing, implementing, and evaluating safety techniques for AI systems, as well as collaborating with cross-functional teams to integrate safety solutions into Meta's products and services.

Computer Software

Comp. & Benefits

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

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 solutions into Meta’s products and services

Lead complex technical projects end-to-end

Qualification

LLM/NLP experiencePython programmingPyTorch frameworkTechnical project leadershipSafety system evaluationDataset curationPhD in relevant fieldPublications in conferencesAI systems

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 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 capabilities (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