Jobs at ServiceNow

Conducts enterprise AI research focused on agentic AI, tacit knowledge, continual learning, resilience, safety, privacy, threat detection, and responsible AI. The role defines and executes research agendas, publishes influential work, develops production-quality research software, collaborates with engineering and product teams, and tests hypotheses in real enterprise deployments. It also requires communicating complex technical concepts to executives and non-specialist audiences.

Conducts advanced research on enterprise agentic AI, including agent memory, continual learning, resilience, safety, privacy, threat detection, and responsible AI. Defines research agendas, publishes influential work, develops production-quality research software, collaborates with researchers and engineers, and influences product and technical decisions. The role tests research hypotheses against real enterprise deployments and communicates complex findings to technical and executive audiences.

Leads advanced AI research on enterprise agentic systems, focusing on tacit knowledge, continual learning, resilience, safety, threat detection, privacy, and responsible AI. Defines research agendas, publishes influential work, validates hypotheses against deployed systems, and provides scientific leadership across research, engineering, and product teams. Requires strong software development, technical influence, and communication skills.

Research and engineer enterprise large language models across data curation, pretraining, fine-tuning, evaluation, training, and inference. Develop and test novel AI/ML methods, build reproducible training pipelines and evaluation harnesses, optimize large-scale distributed training, and collaborate with scientists, engineers, and product managers to deliver production-ready solutions.

Build and deploy end-to-end GenAI and ML systems for strategic customers, including RAG pipelines, agentic workflows, evaluation harnesses, guardrails, observability, and production services. Lead AI discovery workshops, translate business problems into technical solutions, collaborate with engineers, create reusable frameworks, influence product strategy, and enable customer adoption. The role requires strong software engineering, ML, MLOps, responsible AI, platform architecture, and customer-facing delivery skills, with travel up to 30%.

Manages a network reliability and resiliency team, including career development, project prioritization, performance reviews, crisis management, and onboarding. Leads infrastructure reliability initiatives, automation efforts, process improvements, monitoring solutions, training, and adoption of new technologies. Requires hands-on Linux, database, systems, coding, cloud operations, distributed-team, SaaS infrastructure, and ITIL experience. Government of Canada Reliability Status screening is required.