Machine learning engineer

Machine Learning Engineer, DoorDash, Inc., New York, NY. Develop, release, and run large scale web applications. Develop and define the backend architecture and tech stack for a product area. Improve performance, reliability, scalability and security for backend systems. Involved in transitioning monolithic codebase to a microservice- based architect. Completely disrupt logistics by tackling bleeding edge technical problems. Work at the intersection of product and infrastructure to solve key business problems with elegant technical solutions. Collaborate cross-functionally as well as evaluate and execute on tradeoffs to maximize business impact for the company. Design ranking, forecasting, and classification algorithms for consumer growth use cases. And create ML model training and serving components. Test and conduct A/B experiments to validate ML model performance use cases. Participate in any company-wide training programs (WeDash, etc.). Telecommuting Permitted. (MLE-S-102-NY). 40 hrs/week, Mon-Fri, 8:30 a.m. - 5:30 p.m. Salary: $189,592 - $241,900/yr.MINIMUM REQUIREMENTS: Masters degree (or foreign equivalent) in computer science, mathematics, statistics, physics, or closely related discipline and four (4) years of experience in the applied science or software engineering industry including machine learning. In the alternative, employer will accept a PhD (or foreign equivalent) in computer science, mathematics, statistics, physics, or closely related discipline and one (1) year of experience in the applied science or software engineering industry including machine learning. Qualifying experience must include the following skills (which may be gained concurrently); Programming languages such as Java, Python; Stream data processing; Data structure; CI/CD; Docker; SQL or NoSQL Database; Distributed Systems; Building machine learning models and their supporting pipelines in the information retrieval or recommendation field; Algorithm areas like query understanding, personalization, or ranking. Qualifying experience must also include at least one (1) year in the following skills (which may be gained concurrently); One (1) of the following fields: Information Retrieval and Search Engine; NLP, modeling and understanding of natural languages; Develop Machine Learning Toolkit (MLTK), Recommendation system; or Knowledge graph; Building and deploying machine learning systems in production by using frameworks such as PyTorch, LightGBM, Keras, scikitlearn, Splunk, Spark ML, etc.; Building data pipelines (training, feature engineering) at scale using Pyspark and Snowflake SQL, or other similar frameworks. Any suitable combination of education, training and experience is acceptable. Up to 10% domestic travel possible based on business need. To apply, please send resumes to [email protected]. Must reference job code MLE-S-102-NY to be considered.