About the Role, Mission or Department Overview

The New York Times is hiring a Lead Machine Learning Scientist to join the New A.I. Products & Platforms mission. We are a team building the next generation of reader-facing A.I. experiences for one of the world's most trusted news organizations.

You will provide technical leadership to a team of ML scientists developing embedding models and semantic retrieval algorithms to power new A.I. experiences across our products. Your work will allow teams across the company to build, deploy, and manage applications that use large language models to promote our journalism and our business. Your team will design and train embedding models for representation learning and fine-tune language models for custom use cases and content enrichment. You will report to our Director, Machine Learning. This is a hybrid remote/in-office role.

Responsibilities:

• You will lead the design and training of embedding models for representation learning, for example using transformer encoders and Two Tower architectures.
• You will lead the fine-tuning and evaluation of language models for custom use cases and content enrichment.
• You will productionalize embedding models and language models while working closely with engineering teams, to integrate ML and AI into user-facing products throughout The Times
• You will help to define shared practices around evaluation, responsible A.I. use, and what "good" is inside an organization where judgment and independence are important
• You will identify novel project opportunities to solve critical problems and act on those proposals
• You will scope ongoing product improvements, prioritizing requests from your team
• You will build and facilitate relationships across the organization to ensure you meet our projects' goals.
• 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:

• PhD plus 4+ years experience, or 7+ years experience, in machine learning, statistics, computer science, computational social science, or another quantitative/computational discipline
• 4+ years of experience in creatively reframing our challenges as Machine Learning tasks
• 4+ years of experience with data cleaning, preparation, feature engineering, and model selection techniques
• 2+ years of experience coding and deploying in production environments using Python, developing and deploying complex algorithms that are integrated into company process
• 2+ years of experience building production systems that use LLMs, vision-language models, embeddings, or other deep learning models to solve user-facing problems.
• 1+ years of experience mentoring peers or junior ML scientists through pairing and algorithm, model, and code review

Preferred Qualifications:

• 1+ years of experience with information retrieval or search systems
• 2+ years of experience collaborating cross-functionally with product managers and software engineers
• 2+ years of experience scoping and staging work into well-defined milestones and delivering on communicated timelines

REQ-020336

The annual base pay range for this role is between:

$166,000-$205,000 USD

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, 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.

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.