Top AI & Machine Learning Jobs

Leads Toast’s AI and analytics strategy for Enterprise Care. Owns the roadmap, evaluates and deploys AI-assisted support tools, develops operational intelligence products, and partners with Product, Engineering, and Data teams to move pilots into production. Establishes complexity and workload intelligence, assesses AI readiness, translates AI performance into measurable operational outcomes, and builds organizational AI literacy. The role requires extensive enterprise support or service-delivery leadership, hands-on production AI experience, operational data fluency, and the ability to drive trusted adoption among agents and executives.

Design, build, and operate production AI automations and agentic workflows for Toast’s customer care systems. Integrate LLMs with Salesforce Service Cloud, Amazon Connect, and workflow platforms; develop autonomous actions with human oversight; test, monitor, and improve system reliability, accuracy, latency, and cost. Collaborate with software engineers, Salesforce developers, and care operations stakeholders to translate workflows into scalable intelligent solutions.

Design, build, deploy, and maintain resilient, scalable backend services and platform components for Toast's Voice AI platform. Lead cross-cutting architecture and projects, integrate AI platforms (LLMs), implement observability and evaluation for AI systems, set engineering standards, and mentor engineers.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

Build and productionize statistical and ML models for Toast's restaurant platform. Partner with engineers, product, and business teams to mine data, design experiments, evaluate models, and communicate actionable insights that drive product growth and customer outcomes.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, forecasting, targeting, and personalization. Collaborate with engineers and product teams, set modeling and productionization standards, mentor data scientists, guide architecture and experimentation, and apply cloud and MLOps best practices to drive measurable product and business impact.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

Lead design and build of an AI-native engineering OS enabling AI across the SDLC/PDLC. Deliver model access, agent orchestration, prompt/spec frameworks, developer portals, CI/CD re-architecture, governance, cost optimization, and an internal AI marketplace. Build and lead an AI platform engineering org and drive org-wide adoption of standardized AI workflows and tooling.

Lead and build an AI-native engineering OS and developer experience: design model access, agent orchestration, prompt/spec frameworks, CI/CD integration, governance, cost optimization, and developer portals. Architect scalable, model-agnostic platforms, drive org-wide adoption, and manage a team of AI platform and developer-experience engineers while operating within compliance constraints (PCI, SOX).

Lead architecture and implementation of AI agents and infrastructure for the Team domain. Define scalable, secure designs, partner with engineering and product, write code and prototypes, resolve performance bottlenecks, establish agent/ spec-driven patterns, and mentor engineers to deliver mission-critical SaaS systems.