Summer Internship Program
The Atticus Project
2022 Summer Intern Program
The Atticus Project is now accepting Summer Intern Applications for its 2022 Summer Program from law students and high school students.
Who are We?
The Atticus Open Data Project is a collaborative of legal professionals and law students around the world to curate and label an open-sourced training dataset of legal documents for AI research. The Atticus Open Data Project is hosted by The Atticus Project, a non-profit organization whose mission is to accelerate AI development in Legal.
Since our founding in 2020, we've open-sourced Contract Understanding Atticus Dataset (CUAD), an open-sourced training dataset of legal contracts, and the CUAD Labeling Handbook. We are behind the relaunch of the American Bar Association 2021 Public Target M&A Deal Points Study in collaboration with the ABA M&A Committee.
The inability to review large volumes of legal documents timely and accurately has hindered the legal professionals’ ability to keep up with today’s agile and data-driven executive decision making. High-quality and trustworthy AI systems that can understand legal text are urgently needed, but they are dependent upon the availability, quality and transparency of legal documents annotated by legal experts. This can be solved by the legal industry using our collective domain expertise to curate and annotate high-quality, open source labeled datasets.
This summer, we will continue our collaboration with the American Bar Association M&A Committee in collecting deal points for the 2022 Public Target Deal Points Study. Summer interns will be working under the supervision of experienced attorneys from leading M&A law firms and in-house legal counsels to:
learn important legal concepts in M&A agreements via self-paced training modules and live attorney lectures;
annotate specified clauses in M&A agreements using an AI tool; and
attend weekly workshops, group discussions and speaker series on a variety of topics relating to Legal, AI and career development.
Why Should I Apply?
Industry Changer. The inability to review large volumes of legal documents timely and accurately has hindered the legal professionals’ ability to keep up with today’s agile and data-driven executive decision making. By creating a labeled dataset, we are creating a future where attorneys can dedicate their time and brainpower on high-value analytical work instead of menial and time-consuming document review. The Atticus Project is a first in the Legal industry. There are so many unanswered questions that we need to tackle together. We hope you will join us to be a part of the solution to our day-to-day legal challenges.
Knowledge of AI. You will have the opportunities to work with AI tools and collaborate with AI and data scientists and gain a deep understanding of how AI works in legal document review. You will learn to speak the language of the AI developers and researchers.
M&A Knowledge. This program will provide you with the opportunity to learn hands-on skills under the supervision of experienced attorneys, and get certified to demonstrate proficiency in legal document review in reviewing public target M&A agreements.
Shaping the Future of Legal AI Tools. By designing the training dataset, you will have the opportunity to help shape the Legal AI development in a direction that you think is ethical and good for lawyers and our society in general.
6-8 weeks (June and July)
Remote via Zoom.
No. This is an unpaid program. The Atticus Project is a 501(c)(3) organization and hours contributed to The Atticus Open Project are eligible for pro bono hours and Presidential Volunteer Service Awards.
What are the qualifications required?
Motivated law students or high school students passionate about the mission of The Atticus Project
Diligent, persistent and responsive
Thrive despite uncertainty
Proficient with productivity tools and technology
How to apply?
Submit a copy of your resume and cover letter to email@example.com. In your cover letter, please indicate whether you are applying for the Legal Track or the Technical Track.