Ai/ml Engineer Job in Adelement Media Solutions

Job Description Data driven decision-making is core to advertising technology at AdElement. We are looking for sharp, disciplined, and highly quantitative machine learning/ artificial intelligence engineers with big data experience and a passion for digital marketing to help drive informed decision-making. You will work with top-talent and cutting edge technology and have a unique opportunity to turn your insights into products influencing billions. The potential candidate will have an extensive background in distributed training frameworks, will have experience deploying related machine learning models end to end, and will have some experience in data-driven decision making of machine learning infrastructure enhancement. This is your chance to leave your legacy and be part of a highly successful and growing company. Experience 3 - 7 Years Required Skills 2+ years of industry experience with Python in a programming intensive role 1+ years of experience with one or more of the following machine learning topics: classification, clustering, optimization, recommendation system, graph mining, deep learning 2+ years of industry experience with distributed computing frameworks such as Hadoop/Spark, Kubernetes ecosystem, etc 2+ years of industry experience with popular deep learning frameworks such as Spark MLlib, Keras, Tensorflow, PyTorch, etc 2+ years of industry experience with major cloud computing services An effective communicator with the ability to explain technical concepts to a non-technical audience (Preferred) Prior experience with ads product development (e.g., DSP/ad-exchange/SSP) Responsibilities Collaborate across multiple teams - Data Science, Operations & Engineering on unique machine learning system challenges at scale Leverage distributed training systems to build scalable machine learning pipelines including ETL, model training and deployments in Real-Time Bidding space. Design and implement solutions to optimize distributed training execution in terms of model hyperparameter optimization, model training/inference latency and system-level bottlenecks Research state-of-the-art machine learning infrastructures to improve data healthiness, model quality and state management during the lifecycle of ML models refresh. Optimize integration between popular machine learning libraries and cloud ML and data processing frameworks. Build Deep Learning models and algorithms with optimal parallelism and performance on CPUs/ GPUs. Education MTech or Ph.D. in Computer Science, Software Engineering, Mathematics or related fields