Root Sustainability – AI Engineer Technical Assessment

Hey there 👋

First off, we put together this assignment especially for this interview, so if you think things are unclear, don't hesitate to ask us questions.

This assessment represents a slice of the address matching problem we faced at Root Sustainability. Users input addresses into the tool, which need to be mapped to a coordinates for impact calculations. In order to do so, we adopt an external API (Mapbox) to geocode the results. However, after geo-coding, we want to give our users the possibility to review the accuracy of the match we've found. Therefore, we want to develop a similarity score between the input address and the matched address, to support a human review process. By surfacing addresses that have high impact on the total company carbon footprint and have a low similarity score, we can support our users in only having to check the most problematic addresses, instead of having to review what could be hundreds of thousands of addresses.

You will extend a backend that:

• Talks to the Mapbox geocoding API to find candidate address matches
• Computes a similarity score between the original address and the best matched address
• Exposes a small HTTP API that the provided React frontend consumes

The repository is organized as:

• frontend/ – React app (Vite + TypeScript) to visualise and manage addresses
• backend/ – FastAPI starter backend that you will extend
• data/ – Test data to validate your solution
• README.md – this file

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1. Assessment goals

This assignment is not about building the perfect model. It's about showing how you reason about an applied AI problem and translate that reasoning into working software.

You will:

1. Build an address similarity function returning a score between 0.0 and 1.0
2. Do some experimentation and document your reasoning
3. Integrate your solution into the existing frontend/backend setup

Approximate time budget: 3 hours

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2. Assignment

2.1 Wire up Mapbox properly

• Get and configure a Mapbox access token
  (Mapbox requires a credit card even for the free tier; if you prefer not to, contact us and we’ll provide a token.)
• Explore the API and decide how to select the “best” match based on any user input
• Implement your solution in backend/mapbox_client.py

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2.2 Research and improve the address similarity function

We have provided a baseline implementation of the address similarity function in backend/similarity.py.

The address similarity function should:

• Return a value in [0.0, 1.0]
• Represent whether two addresses likely point to the same real-world entity
• Be reasonably robust to:
  • Language differences
  • Spelling variations
  • Capitalization
  • Formatting differences

We would like you to:

• Explore multiple approaches qualitatively
• Pick one final approach, explain why, and implement it in backend/similarity.py
• Document what you tried, what you chose, and what you would explore next in EXPERIMENTS.md

If you want, you can compare your results quantitatively using the dataset in data/addresses.csv which gives an idea, but not a guarantee, of how good your solution is. We care more about your reasoning than about your score versus this baseline.

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2.3 Implement bulk CSV upload

In practice, our users don't add addresses one by one. They upload spreadsheets with hundreds or sometimes hundreds of thousands of rows. We want to support this workflow by allowing a CSV file to be uploaded containing addresses that need to be geocoded.

You should implement:

• A POST /addresses/bulk endpoint that accepts a CSV file upload
• Parse the CSV, extract addresses, geocode them via Mapbox, compute similarity scores, and store the results
• The endpoint should handle errors gracefully (e.g. malformed CSV, missing columns, empty rows)
• Think about what a good response looks like when some rows succeed and others fail

A sample CSV file is provided in data/bulk_sample.csv to test your implementation. The frontend already has an "Upload CSV" button wired up that calls this endpoint.

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3. Backend (FastAPI starter)

A minimal FastAPI backend starter is provided in backend/. It includes:

• Data models for addresses
• An SQLite database and SQLAlchemy ORM model
• Endpoint skeletons matching the contract below
• A naive Mapbox integration and similarity baseline

To run:

cd backend
pip install -r requirements.txt
uvicorn main:app --reload --port 8000

4. Frontend (React)

A small React + TypeScript frontend is provided in frontend/. It allows you to:

• View all addresses in a table
• Select rows via checkboxes
• Add a new address
• Upload a CSV file with addresses in bulk
• Inspect a single address in detail and update it
• Refresh scores for selected or all addresses

4.1 Running the frontend

cd frontend
npm install
npm run dev

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5. Deliverables

Send us a link to a Git repository on the day before your interview containing:

1. Your implementation
2. Any notebooks or extra scripts used during experimentation
3. Your EXPERIMENTS.md

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Good luck! 🔥