Applied AI Engineer Intern

🔥 0 minutes ago

🇺🇸 United States – Remote

💵 $43k - $54k / year

👨‍🎓 Internship

⚪️ Entry-level

🤖 AI Engineer

👻 Ghost score 0%

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Conduent

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 2017

🤝 B2B

☁️ SaaS

🏢 Enterprise

💰 $93M Post-IPO Debt - Conduent on 2025-08

B2B • SaaS • Enterprise

Conduent is...

📋 Description

• Assess document types, metadata, domain concepts, relationships, and quality issues affecting retrieval in a sampled, design-only corpus • Build preprocessing and LLM-assisted extraction workflows for unstructured design artifacts • Create candidate structured representations including concepts, relationships, provenance, and concise summaries, with human validation • Establish an embeddings/vector-index retrieval baseline and compare it with richer approaches • Explore knowledge graphs, knowledge maps, and other structured indexes • Compare structured representations against the vector baseline using evidence • Use sequential LLM-assisted distillation and assimilation to produce a reusable written knowledge digest or equivalent • Demonstrate that retrieved and distilled knowledge is grounded and reusable through identifiers, provenance, and human-checkable excerpts • Define and track retrieval and knowledge-quality measures including relevance, coverage, precision, redundancy, provenance, groundedness, completeness, consistency, and human validation • Review measures with QA practitioners • Document architecture decisions, experiments, results, limitations, failure modes, recommended next steps, and Azure experiment maintenance guidance • Limit work to research, representation, retrieval, assimilation, prototyping, and evaluation; production deployment, tester-agent integration, program operations, and test-case generation are out of scope

🎯 Requirements

• Currently enrolled in a graduate program, or have equivalent preparation, in Data Science, Computer Science, Natural Language Processing, Information Retrieval, Machine Learning, Artificial Intelligence, Engineering, or a closely related field • Coursework, project experience, or demonstrated work involving natural language processing, information extraction, vector search, retrieval-augmented generation as an experimental method, knowledge representation, applied AI for unstructured enterprise text, or experimental design and evaluation • Comfort working with messy, unstructured text and conducting quantitative evaluation and human review—not only developing demonstrations • Ability and willingness to build and empirically compare multiple representations rather than relying on a single vector index • Strong written communication skills and ability to explain technical work clearly to technical and non-technical audiences • Familiarity with cloud platforms, particularly Microsoft Azure, is helpful • Ability to work independently in a remote environment while collaborating effectively, incorporating feedback, and providing regular progress updates • Must be available to work remotely 20 hours per week for the duration of the fall internship program • Work must remain on Conduent-controlled systems and approved Azure resources • Must maintain human review throughout the work • Internship work products are Conduent intellectual property; external disclosure and thesis/course repository use require prior approval

🏖️ Benefits

• Health insurance coverage • Voluntary dental and vision programs • Life and disability insurance • Retirement savings plan • Paid holidays • Paid time off (PTO) or vacation and/or sick time • Mentorship • Networking opportunities • Exposure to senior leadership • Approved project resources provided by Conduent • Hands-on experience applying AI engineering concepts to practical enterprise challenges • Exposure to data preparation, information retrieval, automation, and AI-enabled knowledge management • Practical experience supporting prototypes, evaluations, and proof-of-concept work • Experience collaborating with technical and business stakeholders in a remote environment • Practice documenting results, insights, limitations, and recommendations for future work