{"id":3110038,"url":"https://alion.io/job/meta-research-scientist-intern-ai-alignment","title":"Research Scientist Intern, AI Alignment","company":{"id":20,"name":"Meta","domain":"meta.com","url":"https://alion.io/company/meta","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Career sitemap","truth_index":{"grade":"B","score":78,"open_postings":248,"ghost_share":0,"stale_share":0.887,"repost_share":0,"time_to_fill_p50_days":38,"computed_at":"2026-10-25T05:49:26Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"intern","employment_type":"internship","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Menlo Park, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"C++","optional":false},{"name":"Computer Vision","optional":false},{"name":"GitHub","optional":false},{"name":"Java","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"TensorFlow","optional":false},{"name":"TensorFlow C++","optional":false}],"status":"live","first_seen_at":"2026-10-19T21:26:00Z","employer_posted_date":"2026-10-19","last_verified_at":"2026-10-26T01:48:32Z","board_verified":true,"closed_at":null,"days_open":6,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":6},"description":"Meta Fundamental AI Research (FAIR) is looking for Research Scientist Interns to advance the science of AI alignment and ML systems. Our teams work on making AI systems more reliable, efficient, more measurable, and better aligned with human intent- spanning reasoning and agents, evaluation and robustness, efficiency, safety and security, privacy, world modeling, and the societal dimensions of AI governance.In this role you will embed with a world-class, multi-disciplinary team of research scientists and engineers to pursue an open research question end to end- from problem framing through experimentation to publication. Interns are expected to drive their own project with mentorship, not execute a predefined task list.\nApplicants from a broad range of technical backgrounds are encouraged to apply, including machine learning, natural language processing, computer vision, reinforcement learning, statistics, and the social sciences as applied to AI systems. Our internships are twelve (12) to twenty four (24) weeks long, and we have various start dates throughout the year.\nResponsibilities\nDevelop novel state-of-the-art algorithms and corresponding systems, leveraging various deep learning techniques Analyze and improve various aspects of the corresponding algorithms and systems, including efficiency, scalability, stability, fairness, security, and privacy Perform state of the art research to advance the science and technology of Machine Learning and Artificial Intelligence Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results Publish research results and contribute to research that can be applied to Meta product development\nQualifications\nCurrently has or is in the process of obtaining a Ph.D. degree in Computer Science, Machine Learning, Artificial Intelligence, or relevant technical field Must obtain work authorization in country of employment at the time of hire and maintain ongoing work authorization during employment Experience with Python, C++, C, Java or other related languages Experience with deep learning frameworks such as Pytorch or Tensorflow First-author publication record at a top-tier ML or NLP or Systems venue (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, MLSys) or equivalent Intent to return to degree program after the completion of the internship/co-op Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as publications at leading workshops or conferences such as NeurIPS, ICLR, AAAI, RecSys, KDD, IJCAI, CVPR, ECCV, ACL, NAACL, EACL, ICASSP, CCS, IEEE S&P, MLSys, or similar Demonstrated experience and self-driven motivation in solving analytical problems using quantitative approaches Experience building large-scale machine learning systems and training with large datasets Experience communicating complex research in a clear, precise, and actionable manner Demonstrated software 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