The mission of The New York Times is to seek the truth and help people understand the world. That means independent journalism is at the heart of all we do as a company. It’s why we have a world-renowned newsroom that sends journalists to report on the ground from nearly 160 countries. It’s why we focus deeply on how our readers will experience our journalism, from print to audio to a world-class digital and app destination. And it’s why our business strategy centers on making journalism so good that it’s worth paying for.

The New York Times is seeking inventive and motivated interns to join the Machine Learning Science group. In this role, you will develop and deploy machine learning solutions to newsroom and business problems, to deliver accurate predictions and effective optimization.

About Us

The New York Times is a technology company committed to producing the world’s most reliable and highest quality journalism. Our ability to do so relies on a talented team of expert technologists who help NYT learn from a tremendous abundance of data unique to this company. The Times seeks a ML Scientist Intern for the summer of 2027 to join the Machine Learning Science Group, applying machine learning methods to meet this challenge in close collaboration with working partners across the company.

Check out the Times Open blog to see what we’re up to.

About our Team

We implement and execute machine learning research with reliability and reproducibility. We communicate results and impact to newsroom and business stakeholders. Our group turns models into data products, collaborating with engineering teams, and integrates these into processes throughout The Times.

About Our Summer Internships

The duration is 10 weeks long, starting on June 7, 2027.
The rate of pay for this role is $20.00 per hour.
Interns work 35 hours per week.
Hybrid work based in New York City.

Responsibilities:

Develop and deploy machine learning solutions to newsroom and business problems
Work with Python and tools such as scikit-learn and Google Cloud Platform
Communicate the results of your analyses to other members of the organization in a clear and concise fashion
Demonstrate support and understanding of our value of journalistic independence and a strong commitment to our mission to seek the truth and help people understand the world.

Basic Qualifications:

You have experience in computer science, applied mathematics, or other quantitative or computational disciplines.
You have experience with open source machine learning or statistical analysis tools.
You have coding experience in Python.
You are eager to collaborate with both technical and non-technical colleagues in editorial, product management, marketing, and executive leadership groups.

Preferred Qualifications:

You have experience with experimental design.

REQ-020967

Additional Compensation and Benefits

For roles in the U.S., dependent on your role, you may be eligible for variable pay, such as an annual bonus and restricted stock. Benefits may include medical, dental and vision benefits, Flexible Spending Accounts (F.S.A.s), a company-matching 401(k) plan, employee stock purchase plan, paid vacation, paid sick days, paid parental leave, tuition reimbursement and professional development programs.

For roles outside of the U.S., information on benefits will be provided during the interview process.

The hourly rate of base pay for this role is:

$40—$40 USD

Candidate Use of GenAI Tools

We’re excited to learn more about you and your experience. To keep our hiring process as fair and authentic as possible, we ask that you submit your own work and not use GenAI tools to generate substantive content during the application and interview process.

If you’re an engineering candidate, we’ll let you know what specific GenAI tools you are permitted to use for your technical assessment.

The New York Times Company is committed to being the world’s best source of independent, reliable and quality journalism. To do so, we embrace a diverse workforce that has a broad range of backgrounds and experiences across our ranks, at all levels of the organization. We encourage people from all backgrounds to apply.