Sales Student

CoverQuick is seeking a student to help with our sales and outreach to potential partners in coding boot camps and other related industry leaders. The student will be expected to reach out, and book meetings throughout the project term. Successful completion will require the student to have booked 5 client meetings.

Backend Software Engineering for CoverQuick

The main goal for the project is to complete backend related software engineering tasks using Next.JS and Python for the company CoverQuick, which uses AI to help write resumes and cover letters. This will involve several different steps for the learners, including: - Understanding the current backend infrastructure of CoverQuick - Developing new features and functionalities using Next.JS and Python - Optimizing the backend performance and assessing areas for improvement - Integrating AI capabilities into the backend system - Testing the developed software and making improvements based on user feedback

Growth Hacking Strategy for CoverQuick

The main goal for the project is to create a growth hacking strategy to identify which hacks would be the biggest revenue drivers for CoverQuick, a company that uses AI to help write resumes and cover letters. This will involve several different steps for the learners, including: - Analyzing the current user base and revenue streams of CoverQuick. - Researching and identifying potential growth hacking strategies that can be implemented. - Developing and testing the identified growth hacking strategies to measure their impact on revenue. - Optimizing the most effective strategies for long-term revenue growth.

Computer Vision Model Researcher

The main goal for the project is to develop a computer vision model that can estimate depth and image extrinsics using a provided dataset. The dataset will have all the information needed to train test and evaluate the model. The model should be able to accurately estimate the depth of objects in an image and determine the extrinsic parameters and other variables. This will involve several different steps for the learners, including: Understanding the provided dataset and the format of the data. Preprocessing the dataset to extract relevant features and labels for training the model. Developing and training a computer vision model. Developing and testing an architecture that will be used for the training of the model. Evaluating the performance of the model using appropriate metrics. Optimizing the model for better accuracy and efficiency. Documenting the entire process and presenting the findings.