Job DescriptionAt the New York Times, we use machine learning to enhance the experience of 150 million digital readers (and growing) from around the globe that come to us to seek the truth and understand the world. We use machine learning across our products to build tools that help empower our journalists and grow our subscriber base through content recommendations and personalizations.We are looking for ambitious engineers to join our Machine Learning Infrastructure team to help tackle creative challenges around ML pipelines and backend infrastructure for the Times. A few examples are:Building pipelines to train and test algorithms that provide real-time content recommendationsBuilding ranking and classification APIs to support products at scaleGeneralize and extend our contextual bandits capabilitiesScale and expand feature pipeline and feature store systemsSince our team develops and operates systems in production, you will have ownership of deploying, monitoring, and supporting our apps at scale.By joining our team, you'll help strengthen a company whose mission matters now more than ever. You'll help ensure the journalism of a 1700 person newsroom reaches as many people as possible. In the technology organization, we are always introspective to improve our culture, understand how we work, and be open about how we build things while learning from others. We believe in a diverse environment, and we work hard every single day to make it happen. At the Times, we value everyone's ideas and encourage everyone to bring their ideas out.You'll be an excellent match if you:Are an engineer with at least two years of direct relevant experience in ML or the ML orbit, including experience operating large systems in a production environmentHave experience building real-world machine learning applications (NLP, recommendation systems, bandits, etc.)Are familiar with Python or Go (both are a plus!)Have GCP or other cloud computing platforms experienceHave worked with Kubernetes, Docker, and CI/CDExperience with data processing, validation, and scheduling requirements necessary in an ML production environmentExperience working on cross-functional projectsExperience mentoring other software engineers through design sessions and code reviewsExperience with any of the following technologies and frameworks is ideal but not required: Terraform, Airflow, SQL/BigQuery, CI/CD (Drone), TensorFlow, scikit-learnMost importantly, we're not looking for someone that knows it all just be curious! and know there will be many opportunities to learn and grow in the team. The Times also provides a robust annual tuition reimbursement program for those seeking to continue to develop their skills while with us. This role may require limited on-call hours. An on-call schedule will be determined when you join, taking into account team size and other variables. #LI-AM1The New York Times is committed to a diverse and inclusive workforce, one that reflects the varied global community we serve. Our journalism and the products we build in the service of that journalism greatly benefit from a range of perspectives, which can only come from diversity of all types, across our ranks, at all levels of the organization. Achieving true diversity and inclusion is the right thing to do. It is also the smart thing for our business. So we strongly encourage women, veterans, people with disabilities, people of color and gender nonconforming candidates to apply.The New York Times Company is an Equal Opportunity Employer and does not discriminate on the basis of an individual's sex, age, race, color, creed, national origin, alienage, religion, marital status, pregnancy, sexual orientation or affectional preference, gender identity and expression, disability, genetic trait or predisposition, carrier status, citizenship, veteran or military status and other personal characteristics protected by law. All applications will receive consideration for employment without regard to legally protected characteristics. The New York Times Company will consider qualified applicants, including those with criminal histories, in a manner consistent with the requirements of applicable state and local "Fair Chance" laws.