Top AI & Machine Learning Jobs

Lead design and productionalize ML algorithms and pipelines to detect account-sharing fraud in real time. Build scalable preprocessing, training, deployment, monitoring, and A/B testing solutions with cross-functional teams to optimize detection while minimizing user disruption.

Design, build, and maintain end-to-end ML infrastructure, CI/CD pipelines, model registry and monitoring. Productionize models with data science partners, optimize training and inference workflows, and ensure governance, security, scalability, and cost-effectiveness using cloud and container platforms.

Build and operate Crunchyroll’s AI platform, including automated evaluation, governance, telemetry, policy enforcement, reusable RAG and agent components, and enterprise AI tool administration. Develop business-user agents, manage approved AI tool adoption, integrate security controls, and own platform features end-to-end. Partner across engineering, IT, security, and AI teams while establishing scalable engineering standards and golden paths.

Designs, builds, evaluates, deploys, and maintains production-grade agentic AI systems for business teams. Responsibilities include workflow analysis, AI solution selection, architecture, orchestration, RAG, tool integration, reliability engineering, observability, guardrails, stakeholder management, and post-handoff sustainability. The role requires translating ambiguous business needs into practical technical solutions while managing cost, latency, safety, and operational risks.

Lead research and applied science for personalization and recommender systems across Crunchyroll products. Define problems, build and evaluate models (ranking, retrieval, sequence, bandits, LLM-enabled approaches), design offline/online experiments, collaborate with product and engineering to productionize solutions, and establish best practices for experimentation, reproducibility, and model governance to improve discovery, engagement, and long-term user value.

Lead and grow an engineering team focused on video intelligence analytics, observability, dashboards, anomaly detection, forecasting, and AI-driven insights. Own design, implementation, delivery, and operationalization of analytics systems; collaborate with product, PM, and cross-functional stakeholders; enforce engineering best practices; communicate status to senior leadership; hire and mentor engineers while benchmarking new technologies.

Lead design and delivery of AI-assisted engineering across enterprise projects: build production services, direct and review AI-generated code, implement agent workflows, improve testing/observability, and establish repeatable, secure AI-first delivery practices.

Lead development of production AI-assisted engineering systems: build services, APIs, integrations, and tooling; direct and review AI coding agents and AI-generated code; establish repeatable practices, observability, testing, and secure delivery for enterprise platforms.

Design, deliver, and operationalize LLM- and generative AI-driven platform services. Build scalable model-serving, orchestration, MLOps, observability, data lineage, and evaluation frameworks to productionize multi-step automated workflows.