Senior Machine Learning Engineer, Nexxen

• Bachelor in Computer science or related field of study
• 5+ years of engineering experience working at leading technology companies
• A history of successfully taking ownership of features/functionality and delivering end-to-end project
• Strong knowledge of and experience implementing AI/ML technologies (Neural Network, ONNX), mathematics, and statistics
• Expertise in Java, Python, and Scala
• In-depth knowledge of data structures and algorithms
• Experience building applications on top of large-scale ML/DL frameworks (TensorFlow, PyTorch)
• Expertise working with data processing tech stack (Spark, MapReduce)
• Expertise in building data-driven services and applications (ETL)
• Experienced with interdisciplinary collaborations with Data Science, Product, and Business stakeholders
• A proven track record of thriving in a fast-paced, data-driven, and collaborative work environment is required
• Strong communication skills with the ability to communicate complex concepts to non-technical stakeholders
• We know that not everyone takes the same career path, so if you don’t match this job description perfectly, don’t worry!

Desirable

• The Sr. Machine Learning Engineer will join our Nexxen DSP Software Development group. In this hands-on role, you will work in close collaboration with data scientists to build the next generation of applied data science infrastructure for real-time performance optimization and machine learning solutions
• The right person for this role should be experienced in crafting implementation for either machine learning technologies or data-driven algorithms. If you are someone who is proactive and innovative in these domains, this is a phenomenal role for you!
• Develop critical high-performance, large-scale services for Nexxen DSP Programmatic integrations and Platform product
• Drive ground-breaking innovation and apply state of the art AI and machine learning in a variety of areas to enhance real-time ad scoring and ranking system for bid optimization
• Develop scalable and efficient approaches for large scale data analysis and model development
• Build and experiment new algorithms and models end-to-end, from production rollout to continuous optimization
• Collaborate with data scientists, engineering teams, product managers, and program managers in an open and creative environment
• Help troubleshoot production and customer issues
• Follow agile methodologies and work in a fast-paced environment where one can quickly see the results of the work being performed