About the Company/Team

Adaviv is an MIT-spinout building AI-powered crop intelligence software for specialty agriculture. Our platform helps growers and shippers of berries, tomatoes, avocados, and other high-value crops detect plant health issues earlier, assess harvest quality faster, and make data-driven cultivation decisions. We serve customers including Driscoll’s and NatureSweet across the U.S. and Mexico. We’re a small, high-performance team (6 people) where interns work directly with founders and engineers on production systems - not side projects. Your work will ship to real growers and have measurable impact on food quality and waste reduction.

About the Role

We’re looking for a technically strong, self-motivated intern to contribute across one or more of our core AI and data workstreams. This is a single role with multiple focus area opportunities - you don’t need expertise in all of them. We’ll match your assignment to your skills and interests. Focus Areas: Applied Computer Vision: Fine-tune and deploy CNN/transformer models for plant disease detection, harvest quality assessment, and pest identification on mobile and edge devices. Work with real-world agricultural image datasets across crops and growing conditions. Data Engineering & Backend (AI-Embedded): Build and optimize data pipelines, materialized analytics tables, and cloud infrastructure (AWS, PostgreSQL) that power our plant health analytics engine. Design the data architecture behind health trend scoring, treatment efficacy tracking, and alert generation systems. AI/LLM Integration & Agentic Workflows: Help build and crystallize our Plant Health Co-Pilot: an LLM-powered system that generates daily morning briefs, treatment scorecards, risk digests, and interactive Q&A for growers. This includes prompt engineering, retrieval-augmented generation (RAG) over agronomic knowledge bases, structured-to-narrative pipelines, and automated data visualization generation.

Required Pursuing or recently completed a degree in Computer Science, Data Science, Machine Learning, Statistics, Applied Math, or a related field Strong Python proficiency and comfort working in production codebases Hands-on experience with at least one of: deep learning frameworks (PyTorch, TensorFlow), SQL/database systems, Cloud systems and APIs, or LLM/NLP tools Ability to work independently, communicate clearly, and ship iteratively in a small team Valued (any combination) Experience with computer vision pipelines (CNNs, object detection, image classification, data augmentation) Cloud infrastructure experience (AWS S3, Lambda, SageMaker, RDS, or equivalent) Familiarity with data pipeline tools (Airflow, dbt, materialized views, ETL patterns) Prompt engineering, RAG architectures, or building LLM-powered applications Experience with data visualization libraries (matplotlib, Plotly, D3) or automated report generation Agriculture, biology, or sustainability domain interest or background Spanish language ability (our grower-facing tools are bilingual)