Principal Machine Learning Engineer - RAI

Design and build large-scale systems for machine learning training, evaluation, inference, and production operation. Transform machine learning models and research innovations into reliable, scalable product capabilities. Develop reusable infrastructure, services, and components that support multiple models and workloads. Solve complex engineering problems involving scalability, performance, reliability, and efficiency. Partner with applied scientists and software engineers to integrate modeling advances into production systems. Drive architectural decisions while remaining hands-on in the design and implementation of critical components. Incorporate security, privacy, safety, and Responsible AI considerations throughout system design and development. Provide technical direction and mentorship that raises the engineering bar across the team. Bachelor’s Degree in Computer Science or related technical field AND 6+ 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 7+ years of technical engineering experience, or equivalent experience. Experience building machine learning infrastructure or platforms that support multiple models, teams, or workloads. Experience driving architecture across complex, cross-team engineering initiatives. 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 and pipelines in production. Experience with machine learning systems, distributed systems, software architecture, and system design. Proven ability to build and operate systems that support large-scale machine learning workloads. Ability to solve technically ambiguous problems and personally implement critical systems while influencing broader technical direction.