Currently tracking 121 active AI roles, up 55% versus the prior 4 weeks. Primary focus: Ship · Research.
Meta currently has 129 active AI-related job listings. The hiring is most concentrated in the application stage, representing 22% of the roles, followed closely by agents at 20% and pre-training at 16%. Research is the dominant function, with 63 roles, followed by Engineering with 52. The majority of these positions are located in the United States. Frequent tech tags include agent_orchestration, frontier_research, and multimodal. Over the last 30 days, Meta posted 18 new AI roles, a 31% decrease compared to the previous 30-day period.
Meta currently has 136 active AI-related roles in our index. The most common open titles are: Business Support Engineer (5), AI Research Scientist, Robotics (3), Business Engineer, Business AI (3), Software Engineer, Systems ML (3), AI Research Scientist, VLM (vision language models) (2). Most positions are in Engineering and Research.
Meta's active AI hiring is concentrated in: agents (23%), application (20%), serving infrastructure (16%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Meta is hiring AI talent in: United States (108 roles), United Kingdom (9 roles), France (7 roles), Singapore (6 roles).
Job postings at Meta most frequently mention: Machine Learning, Large Language Models (LLMs), Robotics, Agentic Systems, Generative AI.
In the past 30 days, Meta has posted 67 new AI-related roles. That is a +103% change versus the prior 30 days (33 → 67).
| Title | Stage | AI score |
|---|---|---|
| Research Engineer, Evaluations (Tech Leadership) - Meta Superintelligence Labs Research Engineer role focused on building and curating benchmarks and evaluation environments for advanced AI models (text, vision, audio) within Meta Superintelligence Labs. This role directly impacts AI progress and model development by defining capabilities and ensuring quality and reliability. | Eval GatePost-train | 9 |
| Research Engineer, Preparedness - Meta Superintelligence Labs Research Engineer role focused on evaluating frontier AI systems and risks, developing and refining evaluations for multimodal and agentic models, and producing technical artifacts to inform risk assessments and launch decisions. Requires strong ML engineering and research skills, experience with agentic and multimodal models, and understanding of AI safety and threat models. |
| Eval GateAgent |
| 9 |
| Data Scientist, Evaluations - Meta Superintelligence Labs Meta is seeking a Data Scientist for their Superintelligence Labs to lead the design, validation, and analysis of novel AI evaluations and benchmarks. This role focuses on scientific rigor, measuring frontier AI capabilities, and influencing research directions through data-driven insights and publications. | Eval Gate | 9 |
| AI Research Scientist - MSL FAIR Foundations Research Scientist role focused on designing and developing novel benchmarks and evaluation methodologies for frontier AI capabilities within Meta Superintelligence Labs (MSL). The role involves measuring and understanding AI capabilities, influencing research direction, and collaborating with researchers and technical leadership. Requires a strong publication record and experience in machine learning research, particularly in evaluation and deep learning. | Eval GatePost-train | 9 |
| AI Research Scientist - MSL FAIR Foundations Research Scientist role focused on developing and implementing novel evaluations for frontier AI systems, shaping research direction and model development. Requires strong ML research background, experience with LLM/multimodal evaluation, and publication record. | Eval GatePost-train | 9 |
| Research Engineer - MSL FAIR Foundations Research Engineer role focused on building and curating benchmarks and evaluation environments for advanced AI models (text, vision, audio). The role involves developing novel benchmarks, integrating existing ones, and creating scalable evaluation pipelines and tooling to directly impact research direction and model development. Requires strong ML engineering, Python, PyTorch, and experience with LLM/multimodal evaluation. | Eval Gate | 9 |
| Research Engineer - MSL FAIR Foundations Research Engineer role focused on building and curating benchmarks and evaluation environments for advanced AI models across text, vision, and audio. The role involves developing novel benchmarks, integrating existing ones, and creating scalable evaluation tooling to directly impact research direction and model development. Requires strong ML engineering and research skills, Python proficiency, experience with ML frameworks, and a track record of publications in relevant venues. | Eval GatePost-train | 9 |
| Research Scientist Manager, MetaAI Assistant Measurement Research Scientist Manager to lead a team focused on measurement and evaluation of AI Assistants powered by foundation models. The role involves defining scientific strategy for evaluation, ensuring methodological rigor, and collaborating with product, engineering, and training teams to ensure reliability and trustworthiness at scale. | Eval GatePost-train | 9 |
| Lead, Product Content Engineering Meta is seeking a Lead, Product Content Engineering to manage a team focused on evaluating and improving AI content experiences. This role involves developing strategies for LLM evaluation, leading a team of content engineers, driving sprints for LLM evaluations and tooling, and collaborating with cross-functional teams to align AI behaviors with user expectations. The position requires experience with GenAI products, prompt engineering, content evaluation, and working with ranking systems, with a focus on delivering business impact at scale in a dynamic environment. | Eval Gate | 7 |
| Model Risk and Validation Lead Lead for Model Risk and Validation within Meta Business Services, focusing on risk measurement, diagnostics, and governance of automations and AI/ML systems across finance operations. Requires expertise in risk measurement, technical understanding of AI/ML, and cross-functional leadership to ensure compliance with policies and regulations. | Eval GatePost-train | 7 |
| Lead, Product Content Engineering This role focuses on driving evaluation efforts for LLM models across product teams, specifically within Meta's Product Content Engineering team. The individual will lead sprints, develop automation strategies for manual evaluation tasks, and partner with Product Managers to align resources. The role involves understanding technical product needs, translating them into actionable directions for cross-functional teams, and contributing to content strategies that improve user experience and content discovery. It requires expertise in GenAI products, prompt engineering, annotation, evaluation metrics, and a strong understanding of recommendation systems and content platforms. | Eval GatePost-train | 7 |
| Product Content Engineer This role focuses on defining and implementing content quality standards for AI-powered experiences, specifically within Meta's AI Discovery team. The Product Content Engineer will build frameworks, rubrics, and pipelines to evaluate AI outputs, assess model behavior, and collaborate with cross-functional teams to improve AI content experiences, particularly in search and recommendation systems. | Eval Gate | 7 |
| Director, Product Content Engineering Director, Product Content Engineering at Meta, focused on driving evaluation efforts for LLM models across teams, leading sprints, and devising automation strategies. This role involves improving content discovery and distribution, working with ranking and delivery systems, and defining content strategies to enhance user experience. Requires technical expertise, data analysis, understanding recommendation systems, and cross-functional collaboration. | Eval GateAgent | 5 |
| AI Governance & Automation Manager Meta's Finance team is seeking an AI Governance & Automation Manager to lead the rollout, operationalization, and optimization of their AI governance program. This role ensures AI adoption within Finance is compliant with SOX and other regulations, while enabling autonomy and efficiency. Responsibilities include program rollout, SOX compliance, framework optimization, defining system requirements for the AI governance platform, collaborating on its build and enhancement, developing continuous monitoring features, ensuring technology integration, reporting on program health, and driving adoption through training and change management. The role also involves staying current on AI technologies, regulatory landscapes, and influencing stakeholders across various functions. | Eval Gate | 5 |