Senior Machine Learning Engineer - RAI

Design and build production systems for machine learning training, evaluation, inference, and operation. Transform machine learning models and research innovations into reliable, scalable product capabilities. Own complex engineering projects from technical design through implementation and production deployment. Develop infrastructure, services, and reusable components that support multiple models and workloads. Diagnose and resolve challenges involving system performance, scalability, reliability, and efficiency. Partner with applied scientists and software engineers to integrate modeling advances into production systems. Contribute to architectural decisions and coordinate technical work across components and partner teams. Incorporate security, privacy, safety, and Responsible AI considerations throughout system design and development. Mentor other engineers and contribute to strong engineering practices across the team. Bachelor’s Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. These requirements include but are not limited to the following specialized security screenings: Master’s or Doctorate degree in Computer Science or a related technical field, with a focus on machine learning or related areas, AND 5+ years of technical engineering experience, or equivalent experience. Experience building machine learning infrastructure or platforms that support multiple models or workloads. Experience contributing to architecture and delivering technical initiatives involving multiple components or teams. Experience with distributed systems and large-scale machine learning workloads. Demonstrated interest in AI safety and Responsible AI, including building systems that support the safe and responsible development or deployment of AI capabilities. Demonstrated experience deploying and scaling machine learning models or pipelines in production. Software engineering, system design, and problem-solving skills. Experience building systems for one or more stages of the machine learning lifecycle, such as data processing, training, evaluation, or inference. Ability to independently deliver complex technical projects and collaborate effectively across science and engineering disciplines.