{"id":849951,"url":"https://alion.io/job/neocognition-research-intern","title":"Research Intern","company":{"id":680232,"name":"NeoCognition","domain":"neocognition.io","url":"https://alion.io/company/neocognition","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":4,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-26T05:57:25Z"}},"role":"Early Careers","role_family":"Early Careers","seniority":"intern","employment_type":"internship","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":53000,"max_usd":89000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":408},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Function Calling","optional":false},{"name":"JAX","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multi-Agent Systems","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"TensorFlow","optional":false},{"name":"Fine-tuning","optional":true},{"name":"NLP","optional":true},{"name":"Reinforcement Learning","optional":true}],"status":"live","first_seen_at":"2026-05-01T18:56:04Z","employer_posted_date":"2026-05-01","last_verified_at":"2026-10-26T17:29:50Z","board_verified":true,"closed_at":null,"days_open":177,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":177},"description":"About the Role\nAs a Research Intern at NeoCognition, you'll explore novel ideas and work on longer-term research bets that push the boundaries of LLM agents. This internship is designed for those who want to dive deep into open research problems - from reasoning and planning to multi-agent coordination and evaluation.\nYou'll have the freedom to pursue high-risk, high-reward directions that may not be tied to immediate product needs, but could shape the future of agentic AI systems. Your work will culminate in research prototypes and, ideally, publications that contribute to the broader AI research community.\nYou'll collaborate closely with our research scientists and engineers, receiving mentorship and feedback as you design experiments, build prototypes, and analyze results.\nResponsibilities\nExplore novel research directions in areas such as LLM reasoning, planning, tool use, multi-agent systems, or evaluation methodologies.\n\nDesign and execute exploratory experiments to test new hypotheses and push the boundaries of what agentic systems can do.\n\nBuild research prototypes that demonstrate new capabilities or insights, even if they are not immediately production-ready.\n\nCollaborate with the research team to document findings, analyze results, and iterate on ideas.\n\nWork toward publishing research outcomes in top-tier AI venues (NeurIPS, ICLR, ICML, ACL, etc.) or contributing to open-source efforts.\n\nParticipate in team discussions, paper readings, and brainstorming sessions to shape the research roadmap.\n\nQualifications\nRequired:\nCurrently pursuing or recently completed a PhD, Master's, or advanced undergraduate degree in machine learning, computer science, or a related field.\n\nStrong foundation in machine learning and natural language processing, with demonstrated interest in large language models or agentic AI systems.\n\nProficiency in Python and familiarity with modern ML frameworks (e.g., PyTorch, JAX, or TensorFlow).\n\nAbility to design, implement, and analyze research experiments independently and collaboratively.\n\nStrong written and verbal communication skills, with a passion for sharing ideas and learning from others.\n\nNice to have:\nPrior research experience or publications in AI, NLP, or related areas.\n\nExperience with open-weight models, fine-tuning, or reinforcement learning.\n\nFamiliarity with agent frameworks, tool-use systems, or evaluation benchmarks.\n\nInterest in long-term research bets and 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