Top AI & Machine Learning Jobs

Leads Crunchyroll’s enterprise-wide AI enablement strategy, roadmap, governance, and adoption. Identifies opportunities to redesign workflows with AI, agents, and automation; translates business needs into scalable solutions; partners with Engineering, Data, IT, and Enterprise Technology; manages external experts and vendors; builds AI learning programs, playbooks, communities of practice, and measurable adoption capabilities; and leads a lean, high-performing team.

Leads and mentors Applied Scientists developing, evaluating, and deploying machine learning and language-model solutions for healthcare workflows. Translates clinical and business problems into scalable AI systems, applies rigorous statistical methods and experimentation, partners with engineering and cross-functional stakeholders, and ensures models are reliable, reproducible, and safe. The role also contributes hands-on technical work, recruiting, onboarding, and team development.

Build and manage AI-powered workflows, agents, automations, and internal applications for Deal Desk and GTM teams. Identify automation opportunities, develop prototypes, scale solutions to production, integrate enterprise data and APIs, implement human validation and audit controls, optimize LLM costs, document tools, measure adoption, and train users. The role requires Deal Desk or RevOps experience plus practical knowledge of AI models, APIs, scripting, SQL/SOQL, Python, CPQ, CLM, Salesforce, and agentic platforms.

Conduct applied AI and machine learning research spanning multimodal intelligence, recommendation, ranking, prediction, foundation models, agents, and decision-making. Define research problems, evaluate and develop models using proprietary data, run rigorous experiments, create training signals and evaluations, and transition successful approaches into production systems. Collaborate with engineering and product teams while shaping the company’s research agenda and technical standards.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Provide technical leadership and build core components for a scalable AI application platform. Design distributed systems infrastructure, improve performance and reliability, shape architecture and development practices, conduct code reviews, mentor engineers, and lead complex projects from conception through delivery. Collaborate with product, engineering, and design teams to evolve the platform and enhance developer experience.

Build and productionize AI agents for customer support, voice interactions, and carrier onboarding. Develop primarily backend applications in Python and Java or Go, design databases and APIs, integrate frontend components, troubleshoot production workflows, conduct code reviews, and deploy services with Kubernetes. Collaborate with product, engineering, DevOps, US teams, and customers to deliver supply chain platform capabilities.

Senior Data Scientist responsible for partnering with business leaders to frame ambiguous problems, deliver statistical and machine learning solutions, communicate insights, and drive measurable adoption. The role includes end-to-end analytical ownership, stakeholder influence, executive communication, Agile backlog management, cross-functional collaboration with engineering and technology teams, mentorship, governance, and improving analytics maturity.

Leads and scales AI engineering teams responsible for production LLM systems, agents, integrations, and shared AI platforms. Oversees architecture, delivery, reliability, cost, latency, evaluation, observability, governance, security, and operational practices. Partners with product and business leaders to prioritize investments, manage dependencies, and establish reusable engineering standards. Develops engineering leaders and drives alignment across teams while ensuring safe, scalable AI adoption.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.