{"id":3702781,"url":"https://alion.io/job/harvard-university-scientific-ai-engineer","title":"Scientific AI Engineer","company":{"id":241,"name":"Harvard University","domain":"harvard.edu","url":"https://alion.io/company/harvard-university","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":{"grade":"B","score":75,"open_postings":18,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":29,"computed_at":"2026-11-12T10:38:37Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Boston, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":true,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Bioconductor","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"NumPy","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SciPy","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"R","optional":true}],"status":"live","first_seen_at":"2026-11-03T18:24:10Z","employer_posted_date":"2026-11-03","last_verified_at":"2026-11-13T15:24:59Z","board_verified":true,"closed_at":null,"days_open":9,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":9},"description":"By working at Harvard University, you join a vibrant community that advances Harvard's world-changing mission in meaningful ways, inspires innovation and collaboration, and builds skills and expertise. We are dedicated to creating a diverse and welcoming environment where everyone can thrive.\nWhy join Harvard Medical School?\nHarvard Medical School's mission is to nurture a diverse, inclusive community dedicated to alleviating suffering and improving health and well-being for all through excellence in teaching and learning, discovery and scholarship, and service and leadership.\nYou’ll be at the heart of biomedical discovery, education, and innovation, working alongside world-renowned faculty and a community dedicated to improving human health. This is more than a job - it’s an opportunity to shape the future of medicine.\nAbout the Department of Immunology:\nPosition open for a Scientific AI Engineer, to work in the context of the ImmGen consortium at Harvard Medical School. ImmGen is an NIH-funded consortium of Immunology and Computational biology labs who pool expertise to decipher gene expression and it regulation in the immune system, whose data have become a valuable resource for the Immunology research community worldwide. The group wishes to develop a data analysis AI agent that will harness ImmGen data together with external databases to answer user questions formulated by scientists in natural language and return data-grounded visualizations and contextualization.\n We are seeking an autonomous Scientific AI Engineer who will work closely with the existing ImmGen computational team and immunologists to design and implement this agent. They will integrate ImmGen’s existing data resources and analytical capabilities with the AI-powered interface. They will not be expected to acquire or manage ImmGen data, nor rebuild analytical tools. Instead, they will determine how to expose, adapt, orchestrate, and extend those tools so they can be reliably accessed through natural-language interaction, using MCP strategies that can be exploited by AI agents.\nThe position will:\nWork closely with ImmGen scientists and data analysts to understand existing datasets, metadata, documentation, tools, APIs, and access mechanisms, adapt and wrap existing ImmGen data so they can be invoked reliably by LLM-based agent that calls on public analytical and visualization tools.\nCollaborate with ImmGen scientists to define supported use cases, desired answer formats, and appropriate scientific language and graphics.\nMap existing and potential researcher questions to the appropriate existing ImmGen resources and analytical workflows.\nDevelop the software interfaces, APIs, schemas, computational guardrails and orchestration logic required to connect the agent to ImmGen’s existing infrastructure.\nImplement safeguards against hallucinated results, invalid comparisons, inappropriate statistical interpretations, and unsupported biological claims.\nEnsure that the agent does not automatically initiate unduly expensive computes.\nWe are looking for a team player eager to participate in the exciting re-writing of scientific data propagation.\n Basic Qualifications:\nMinimum of five years’ post-secondary education or relevant work experience.\nAdditional Qualifications and Skills:\nAdvanced degree or equivalent professional experience in computational biology, bioinformatics, genomics, or a related field.\nStrong Python programming skills and experience with scientific computing libraries such as pandas, NumPy, SciPy, scikit-learn, Scanpy, Bioconductor, or equivalent tools.\nStrong software-engineering skills, including experience with APIs, data schemas, testing, version control, logging, documentation, and reproducible workflows.\nExperience integrating software with existing scientific data platforms, analytical pipelines, web services, or computational tools.\nHands-on experience with LLM applications, including tool/function calling, retrieval-augmented generation, structured outputs, agent orchestration, or natural-language-to-SQL/code systems.\n Term: This is a 2-year term position from the date of hire, with the possibility of extension, contingent upon work performance and continued funding to support the position.\nStandard Hours/Schedule: 35 hours per week.\nVisa Sponsorship Information: Harvard University is unable to provide visa sponsorship for this position.\nPre-Employment Screening: Identity\n Interview Format: The interview process will include both virtual and in-person components (e.g., initial virtual screening followed by on-campus final interviews).\nStaying Informed About Your Application: Due to the high volume of applications, we may not always be able to reach out right away, but you can track your status anytime through the Careers@Harvard portal.\nWork Format Details\nThis position has been determined by school or unit leaders that all duties and responsibilities must be performed at a Harvard or Harvard-designated location. Certain visa types may limit work location. Individuals must meet work location sponsorship requirements prior to employment.\nSalary Grade and Ranges\nThis position is salary grade level 057. Please visit Harvard's Salary Ranges to view the corresponding salary range and related information.\nBenefits\nHarvard offers a comprehensive benefits package that is designed to support a healthy work-life balance and your physical, mental and financial wellbeing. Because here, you are what matters. Our benefits include, but are not limited to:\nGenerous paid time off including parental leave \nMedical, dental, and vision health insurance coverage starting on day one \nRetirement plans with university contributions \nWellbeing and mental health resources \nSupport for families and caregivers \nProfessional development opportunities including tuition assistance and reimbursement \nCommuter benefits, discounts and campus perks \nLearn more about these and additional benefits on our Benefits & Wellbeing Page.\nEEO/Non-Discrimination Commitment Statement\nHarvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard's academic purposes.\nHarvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university's non-discrimination policy. 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