PhD University Grad Machine Learning Engineer 2026 (USA) at PRINTEREST INC

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

08 Jan, 26

Salary

214060.0

Posted On

10 Oct, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Natural Language Processing, Graph Analysis, Data Analysis, Modeling, Ranking, Big Data Technologies, Java, C++, Python, Tensorflow, Pytorch, MLFlow, Research, Communication, Teamwork

Industry

Software Development

Description

About Pinterest: Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product. Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible. With more than 570 million users around the world and 300 billion ideas saved, Pinterest Machine Learning engineers help build personalized experiences to help Pinners create a life they love. With just over 3,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you’ll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won’t find anywhere else. By applying to this role, you will be considered for multiple roles open across our various ML teams. Please only apply once within the USA or Canada as multiple applications may delay our recruitment process. This is a general posting for multiple roles open across our various ML teams. You will have the opportunity to work on a very large scope of problems in recommender systems, search, ads, ranking, natural language processing, graph representation learning, personalization, etc. As a Machine Learning Engineer at Pinterest, you’ll work on tackling new challenges in machine learning and artificial intelligence. As you kickstart your career at Pinterest, you’ll join our engineering teams as we maneuver through exponential growth and massive scale while building awesome products and features, creating visually rich experiences, spearheading the discovery problem, and pinpointing tomorrow’s engineering challenges. What you’ll do: Contribute to cutting-edge research in machine learning and artificial intelligence that can be applied to Pinterest problems Collect, analyze, and synthesize findings from data and build intelligent data-driven model Write clean, efficient, and sustainable code Use machine learning, natural language processing, and graph analysis to solve modeling and ranking problems across discovery, ads and search Design, build, and test models to predict engagement for notifications (push, emails, in-app notifications) Build content recommendation systems to power our push, email, and in-app notifications Work on state-of-the-art large-scale applied machine learning projects Scope and independently solve moderately complex problems What we’re looking for: PhD in Computer Science, ML, NLP, Statistics, Information Sciences or related field required Machine Learning experience (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc) Proficiency in at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow) Experience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data Experience in research and in solving analytical problems Strong communicator and team player with the ability to find solutions for open-ended problems. Preferred Qualifications: Publications in machine learning, AI, data science, data analytics, statistics, or related technical fields Interest in research and in applying ML to impactful real-world problems on the Pinterest product In-Office Requirement Statement: We let the type of work you do guide the collaboration style. That means we’re not always working in an office, but we continue to gather for key moments of collaboration and connection This role may require you to be located near an office for in-person collaboration, and therefore may need to be located a commutable distance from one of our Pinterest offices. #LI-HYBRID #LI-EB1 At Pinterest we believe the workplace should be equitable, inclusive, and inspiring for every employee. In an effort to provide greater transparency, we are sharing the base salary range for this position. The position is also eligible for equity. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise. Information regarding the culture at Pinterest and benefits available for this position can be found here. US based applicants only $162,690—$214,060 USD Our Commitment to Inclusion: Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, ancestry, national origin, religion or religious creed, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, age, marital status, status as a protected veteran, physical or mental disability, medical condition, genetic information or characteristics (or those of a family member) or any other consideration made unlawful by applicable federal, state or local laws. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require a medical or religious accommodation during the job application process, please complete this form for support.

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Responsibilities

Contribute to cutting-edge research in machine learning and artificial intelligence applicable to Pinterest problems. Design, build, and test models to predict engagement and develop content recommendation systems.