Top Entry Level Artificial Intelligence Jobs

Own customer deployments of an AI-powered control-testing platform, including configuration, control prioritization, testing attributes, samples, and integrations. Train and coach internal audit teams, advise on control testing and evidence sufficiency, support AI-assisted audit reviews, resolve customer issues, and relay product feedback to engineering. Build deployment playbooks and help-center content while managing multiple customer accounts.

Develop and deploy reinforcement learning and imitation learning policies for whole-body humanoid locomotion and manipulation. Build high-fidelity simulation environments, improve sim-to-real transfer, conduct experiments, evaluate models, write reusable code, contribute to publications, and deliver policies to production customer sites. The role spans robotics research, computer vision, model development, and end-to-end deployment.

The Learning Partner supports NYC schools implementing AI literacy and instructional innovation. Responsibilities include building school relationships, designing and facilitating professional learning, coaching educators, supporting AI adoption, co-designing AI tools and learning experiences, guiding school leaders through change management, and sharing field insights to improve Playlab’s platform and resources.

Manage an established portfolio of legacy clients, driving retention, growth, and expansion. Audit account health, conduct strategic check-ins and workshops, advise business owners on offers, funnels, sales, prospecting, closing, fulfillment, and AI adoption, identify upsell opportunities, resolve issues, coordinate cross-functionally, and document account activity.

Own the end-to-end user experience for a technical B2B SaaS product. Responsibilities include researching user friction, observing sessions in FullStory, prototyping flows in Figma or code, refining interaction patterns, implementing design improvements, building reusable design systems, partnering with engineers, and conducting usability tests. The role emphasizes UX refinement, edge cases, loading and empty states, smooth transitions, and incorporating user feedback.

Own Synera’s data warehouse and ingestion infrastructure, integrating GTM and business systems into reliable, documented, monitored pipelines. Build data models, reporting and forecasting layers, and agent-ready interfaces using APIs, MCP connectors, permissions, and governance. Develop retrieval infrastructure for documents and unstructured content, including chunking, embeddings, and indexing. Partner with teams across the company to improve data quality and make business information usable by AI agents.

Own end-to-end people operations across the US and UK, including payroll, benefits, onboarding, offboarding, compliance, employee data, and lifecycle processes. Build scalable systems, automate repetitive workflows, administer Rippling, coordinate with external providers, and serve as the primary resource for employee questions. The role requires hands-on execution, strong attention to detail, discretion with confidential information, and clear communication in a rapidly growing AI startup.

Build and maintain scalable full-stack applications across frontend and backend systems. Own features end-to-end, including user interface design and database optimization, while debugging production issues. Collaborate with founders on technical vision and roadmap, help establish engineering culture, engage with users, and contribute to the growth of an early-stage AI-powered insurance brokerage.

Drives new SaaS business among small healthcare practices by partnering with another account executive, managing opportunities through the sales cycle, conducting consultative sales activities, maintaining pipeline data, meeting revenue targets, and coordinating customer onboarding transitions.

Implements industrial data integrations connecting shopfloor machines, PLCs, sensors, and enterprise platforms through Litmus Edge and IIoT technologies. Configures industrial protocols, maps and normalizes data, maintains edge deployments, troubleshoots connectivity and data quality, and supports predictive maintenance use cases. The role also involves customer engagement, demonstrations, implementation handoffs, documentation, and travel to manufacturing sites.