About the job
Meta is seeking Research Engineers to join the Preparedness team within Meta Superintelligence Labs. The Preparedness team evaluates the increasing capabilities of our AI systems, with a focus on frontier AI capabilities and risks. We ensure that evaluations are in place to mitigate these risks and responsibly handle the development of frontier AI.
Responsibilities
Build and continuously refine evaluations for multimodal and agentic frontier AI models, including in cybersecurity, chemical security, and biosecurity
Build robust, reusable evaluation pipelines that scale across multiple model lines and product areas
Produce auditable technical artifacts, including evaluation reports and model cards, at high reliability and speed
Scope and deliver end-to-end evaluations under ambiguous and rapidly shifting requirements, re-prioritizing as the threat landscape and Meta’s frontier models evolve
Work across research, engineering, policy, and legal teams to align evaluation priorities with launch timelines
Qualifications
Minimum
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
3+ years of experience in machine learning engineering, machine learning research, or a related technical role
Proficiency in Python and experience with ML frameworks
Experience identifying, designing and completing medium to large technical features independently, without guidance
Proven experience in software engineering practices including version control, testing, and code review practices
Preferred
Experience implementing or developing benchmarks for agentic large language models and multimodal models (e.g., vision-language, audio, video, browser agents)
Publications at peer-reviewed venues (NeurIPS, ICML, ICLR, ACL, EMNLP, or similar) related to language model evaluation, AI safety, or deep learning
Experience working with large-scale distributed systems and data pipelines
Experience in red-teaming AI systems, adversarial machine learning, or abuse prevention systems
Background in biology or chemistry, particularly chemical, biological, radiological, and nuclear (CBRN) risk domains and experience designing evaluations or threat assessments related to dual-use scientific knowledge
Background in cybersecurity, penetration testing, or security research, particularly as it relates to assessing AI-enabled cyber capabilities or designing mitigations for AI-assisted exploitation
Track record of open-source contributions to ML evaluation tools or benchmarks