Machine Learning Engineer

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.

Job Duties: Be responsible for the development of state-of-the-art applied machine learning projects for one of the 3 modeling problems in ads ranking: lightweight ranker, engagement modeling and conversion modeling. Design features and build large-scale machine learning models to improve user ads action prediction with low latency. Develop new techniques for inferring user interests from online and offline activity. Mine text, visual, user signals to better understand user intention. Work with product and sales teams to design and implement new ad products. Partial telecommuting permitted.

Minimum Requirements: PhD, or foreign equivalent, in Computer Science, Information Systems, or a closely related field, and six months of experience in job offered or related software engineering/machine learning engineering role.

Special Skill Requirements:

(1)            Experience processing and analyzing large-scale structured and unstructured datasets used in machine learning and recommendation systems, including building and maintaining distributed data pipelines, performing data extraction and transformation, aggregating high-volume user interaction data, and ensuring data quality and scalability for model training and evaluation.

(2)            Applying machine learning techniques to design, build, train, evaluate, and improve predictive models for recommendation and ranking systems, including feature engineering, algorithm selection, model validation, hyperparameter tuning, and iterative optimization based on business and performance metrics.

(3)            Developing and deploying deep learning models for large-scale recommendation use cases, including designing neural network architectures, training models on high-dimensional data, optimizing model performance, and improving recommendation quality through representation learning, embedding-based methods, and other deep learning approaches.

(4)            Using natural language processing techniques to extract, represent, and model textual information for recommendation and related machine learning applications, including text preprocessing, tokenization, embedding generation, semantic modeling, and applying language-based features to improve relevance, ranking, personalization, or content understanding.