Top AI & Machine Learning Jobs

Lead development of open banking data science solutions (affordability/credit decisioning, identity/income verification). Apply end-to-end data science techniques, build/deploy dashboards, run customer trials, support deployment/MLOps, collaborate with stakeholders and Mastercard data science community to drive product excellence.

Lead data-science work for matching features: translate product goals into data problems, design and analyze A/B tests, perform exploratory analysis and data mining, generate strategic insights, and collaborate with AI, product, design, and engineering to develop and iterate production ML models.

Embed with teams to identify high-leverage opportunities and build internal AI-powered products, automations, and data workflows; partner with engineering and product on best practices, reporting, and delivery; lead change management and occasionally ship code to drive adoption and operational improvements.

Lead transition of marketing workflows to AI-driven systems: identify manual processes, build agentic automations for content, segmentation, reporting, and lead enrichment, design data pipelines and a centralized AI-queryable knowledge foundation, automate campaign analytics, and partner cross-functionally to connect data and systems that drive pipeline and revenue.

Design, build, and deploy agentic AI solutions for marketing use cases. Integrate LLMs with enterprise data and MarTech systems, develop cloud-native services/APIs, automate workflows, and drive AI engineering standards and modernization across the MarTech ecosystem.

Lead web and marketing analytics: define tracking specs, implement and validate scalable tracking, join web and game data, build dashboards and reports, deliver campaign-level insights, measure feature effectiveness, and partner with stakeholders to make data actionable.

Lead Product Manager to define, build, and ship enterprise-grade AI agents for financial audit workflows. Partner with engineers and customers to design, deliver, and scale agentic features across audit lifecycle, drive roadmap, and collaborate with cross-functional teams to ensure adoption and impact. Hybrid role based in San Francisco.

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

Develop, evaluate, and deploy machine learning models for real trading environments; apply rigorous statistical techniques to measure performance, control overfitting, and assess model risk and interpretability; collaborate with traders and developers; maintain best practices and production-ready systems.

Design, build, and operate AI agents and decision-support systems embedded in GTM workflows. Conduct stakeholder research, prioritize problems, ship and iterate on agent outputs, monitor adoption and quality, and scale reusable solutions across sales and customer-success motions while staying closely embedded with field teams.