AI & Technical Research Fellow - Volunteer

AI & Technical Research Fellow — Volunteer

Company: Traelli

Location: Remote / HybridCommitment: Approximately 4–6 hours per weekDuration: 8–12 weeksPosition Type: Volunteer FellowshipAbout TraelliTraelli is building a technology-enabled service infrastructure designed to make technical home services more reliable, transparent, and operationally intelligent.Our initial focus is on AC, electrical, and plumbing services, with technology being developed across customer triage, technician decision support, intelligent dispatch, parts intelligence, service documentation, and operational analytics.Rather than using AI simply as a conversational layer, Traelli is exploring how AI can improve real-world service decisions — from understanding incomplete customer-reported symptoms to helping technicians arrive better prepared and improving first-time-fix outcomes.About the RoleWe are looking for an AI & Technical Research Fellow who is curious about how artificial intelligence can be applied to real operational problems.This is a research-oriented volunteer fellowship where you will work alongside the founder on selected technical challenges related to Traelli’s AI and decision-support architecture.You will not be expected to build production systems independently.

Instead, your role will focus on researching technologies, evaluating approaches, designing experiments, developing test scenarios, and helping translate emerging AI capabilities into practical product opportunities.What You May Work OnDepending on your background and interests, projects may include:Multimodal AI for interpreting text, images, voice, and video related to technical service problemsAI-assisted symptom triage and confidence-based decision systemsOCR and equipment model-number recognitionComputer vision applications for HVAC, electrical, and plumbing environmentsRetrieval-Augmented Generation (RAG) for technician knowledge systemsAI-assisted technician troubleshooting and field decision supportStructured evaluation of LLMs and multimodal modelsSynthetic test-case and evaluation-dataset creationAI hallucination reduction and confidence-gating strategiesParts recognition and equipment compatibility intelligenceApplied AI architecture comparisonsAgentic AI workflows for service operationsResearch into emerging technologies relevant to field-service platformsTypical DeliverablesFellows may contribute through:Technical research briefsAI model and architecture comparisonsProof-of-concept recommendationsEvaluation frameworksTest scenarios and edge-case librariesSynthetic datasetsTechnical documentationLiterature and technology reviewsProduct feasibility assessmentsRecommendations for future experimentsThe emphasis is on clear reasoning and practical applicability, rather than research for research’s sake.Who Should Apply?This opportunity may be particularly suitable for:Computer Science studentsArtificial Intelligence / Machine Learning studentsData Science studentsSoftware Engineering studentsEngineering graduates interested in applied AIResearchers exploring multimodal AI, computer vision, LLMs, RAG, or agentic systemsSelf-taught developers or researchers with strong technical curiosityYou do not need to be an expert in every technology listed above.We care more about your ability to research deeply, challenge assumptions, structure technical thinking, and communicate what you learn clearly.What We ValueWe particularly value people who:Think from first principlesAre comfortable saying “the evidence is insufficient”Understand that AI confidence is not the same as correctnessCan compare technologies objectively rather than following hypeEnjoy solving messy real-world problemsCan convert research into actionable recommendationsAre intellectually curious and willing to challenge existing assumptionsWhat You Will GainThis fellowship provides exposure to the early technical development of an AI-enabled service platform and the opportunity to work on problems involving the intersection of:AI × Field Service × Operations × Computer Vision × Decision Systems × Marketplace InfrastructureStrong contributors may receive:Fellowship completion certificateLinkedIn recommendation based on contributionPortfolio-ready research work where confidentiality permitsDirect exposure to startup product and AI architecture decisionsConsideration for future paid internship, research, or technical opportunities at Traelli as the company growsImportantThis is a volunteer, project-based fellowship and is not intended to replace a salaried operational or engineering position.The fellowship is designed primarily for learning, research, experimentation, and contribution to selected non-production technical initiatives.How to ApplyPlease send:1. A short introduction about yourself2. Your LinkedIn profile / CV3. GitHub, portfolio, research work, or projects if available4. The AI area that interests you most5. A brief answer to:“What is one practical problem in field services that you believe AI can solve better today than it could three years ago — and why?”We are more interested in how you think than how many buzzwords you know.Help us explore how intelligent systems can make real-world technical services more reliable.