Research Engineer - AI Safety Systems and Guardrails

We are seeking an experienced Research Engineer to join the System Safety team within Product & Applied Research org at Meta Superintelligence Labs. This role will focus on researching and developing advanced machine learning models, novel algorithms, and scalable systems that improve the safety of Meta's AI products, including Meta AI and conversational, multimodal, and agentic experiences.

You will bring current AI safety research into practical product safeguards, with a primary focus on guardrails, policy-adaptable risk detection, adversarial robustness, and safe agent behavior. This is a hands-on technical leadership role with end-to-end ownership of a significant safety area—from setting research direction and developing methods through rigorous evaluation, cross-team integration, and continuous improvement in production.

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Proven track record of research in one or more of the following areas: AI safety, deep learning, adversarial robustness, language models, multimodal machine learning, or agentic AI, demonstrated through original or materially improved methods and rigorous experimental evidence 3+ years of experience in deploying machine learning models and systems at scale in an industry setting Demonstrated up-to-date expertise in relevant machine learning research advancements, including the ability to reproduce, critically evaluate, and adapt emerging methods to real-world systems Hands-on programming experience in Python and deep-learning frameworks such as PyTorch, including developing, training or fine-tuning, debugging, and evaluating machine learning models Demonstrated experience independently defining technical direction and leading complex research or machine learning initiatives end-to-end, aligning multiple teams and delivering measurable product or platform impact Proven ability to communicate complex technical concepts, influence cross-functional technical decisions, and mentor engineers or researchers Experience developing AI safety guardrails, policy-adaptable classifiers, reliable model-based judges, or adversarial defenses for deployed AI systems Advanced degree (MS or PhD) in Computer Science, Machine Learning, AI, or a related technical field, or equivalent advanced research experience Expertise in safety data generation, adversarial evaluation, calibration, or in multilingual and multimodal safety modeling Experience with agent safety, prompt-injection defense, multi-turn risk detection, tool-use controls, or security-informed threat modeling A strong record of relevant research contributions, such as peer-reviewed publications, adopted open-source methods, or widely used systems supported by rigorous evaluation Experience with efficient model inference, distillation, cascaded models, or distributed ML systems under latency, throughput, cost, and reliability constraints