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.

Responsibilities

Research and develop state-of-the-art machine learning models and novel algorithms for AI safety, with a primary focus on guardrails, risk detection, policy adherence, and adversarial robustness
Design, prototype, and implement safety systems for language models, multimodal models, and AI agents, addressing harmful assistance, jailbreaks, prompt injection, unsafe tool use, and unauthorized information disclosure across individual interactions and complete agent tasks
Set the technical direction and research roadmap for a significant system safety area, leading complex initiatives across research, alignment, evaluation, product, and infrastructure teams
Architect and optimize reusable safety components and machine learning pipelines, combining learned detection with explicit runtime controls and delivering scalable, low-latency safeguards for real-world applications
Develop high-quality training data, synthetic and adversarial examples, and representative benign cases; conduct rigorous experimentation, ablation studies, and independent held-out evaluation to improve safety, generalization, and robustness
Measure the effectiveness of safeguards within the fully deployed system, balancing risk reduction with false refusals, legitimate task completion, privacy, latency, cost, and reliability
Collaborate closely with model researchers, product managers, engineers, and evaluation, security, and policy partners to translate product needs and safety requirements into validated, production-ready solutions
Monitor, maintain, and iterate on deployed safety models and systems, using approved data and privacy-preserving workflows to identify emerging failure patterns, create regression tests, and drive lasting system or model improvements
Stay at the forefront of research and industry trends in AI safety, LLMs, multimodal modeling, adversarial robustness, and agentic systems; reproduce, critically evaluate, and extend promising methods for product deployment. Mentor engineers and researchers to influence architecture and research decisions, and communicate findings through technical documentation, internal presentations, and external publications where appropriate

Qualifications

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