Parse AI provides a machine learning-based cloud platform that enables users to expedite title research necessary for completing a wide variety of real property transactions. Customers in the energy and real estate industries gain a comprehensive perspective on leasehold ownership and obligations and can confirm real property ownership at a fraction of the time and cost.

You will work with a team of ML engineers under the company CTO to develop our internal machine learning capabilities using real-world datasets from day one and be responsible for improving our ML pipeline.

This will involve several different steps for you:

• Conduct background research on our existing products and the dataset.
• Research the latest AI / ML techniques & LLMs and how they could be applied to our data.
• Help build and analyze a custom dataset to train LLMs.
• Integrate LLMs that provide unique outcomes or insights into our data.
• Provide multiple solutions that can be applied to solve the same problem.

Prior experience:

• Python

By the end of the project, you should be able to demonstrate:

• Understanding of the available dataset
• Understanding of the latest LLMs
• Identification of ways in which LLMs can be applied to our company

Bonus steps would include:

• Providing multiple versions of potential integration plans and processes

Final deliverables should include

• A final report on the dataset, the problem solved, methodologies and approaches, outcomes and results, and recommended next steps.
• Source materials such as code and workbooks.

Students will connect directly with our CTO for mentorship throughout the project. We will be able to provide answers to questions such as:

• Parse AI's current products and applications of AI / ML
• Our current land contract data sets and guidance in navigating through them
• Current industry standard approaches to AI / ML
• Input on choices, problems, or anything else the students might encounter.