Senior Software Engineer : AI/ML

Senior Software Engineer : AI/ML About the role: Partners with stakeholders to design, develop, optimize, and productionize machine learning (ML) or ML-based solutions and systems that are used within a team to solve complex problems with multiple dependencies. This role also leads team efforts to leverage and improve ML infrastructure (MLOps) for model development, training, deployment needs and scaling ML systems. About the Team: We are a team within the Uber AI Platform that is paving the way for Data-centric ML. We pioneered the use of feature store as a fundamental building block of the ML lifecycle across the industry - which powers models at Uber for training and serving at millions of QPS with very low latencies. Our ongoing mission is to develop MLOps infra and tools to create high quality features and labels for use in machine models, with the goal of fully automating the lifecycle of feature engineering. We work closely with Data Science and ML teams across Uber to develop algorithms and tooling for sharing, discovery, computation, selection, transformation and monitoring of features and embeddings. Minimum qualifications: PhD or equivalent in Computer Science, Engineering, Mathematics or related field OR 3-years full-time Software Engineering work experience, WHICH INCLUDES 2-years total technical software engineering experience in one or more of the following areas: Programming language (e.g. C, C, Java, Python, or Go) Training using data structures and algorithms Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning) Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib Note the 2-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these). The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated. Technical skills: Required: Scalable ML architecture Feature Engineering Lifecycle Preferred: Applying Information Theory concepts in building scalable feature selection algorithms Experience with building feature embeddings from high dimensional feature sets. Demonstrated use of ML techniques in Personalization or Recommendation Systems Passion for MLOps, comfortable programming in Spark or other large scale distributed processing frameworks