Top AI & Machine Learning Jobs

Lead design and implementation of cloud-first, AI-integrated full-stack applications on GCP. Drive rapid prototyping and MVPs, mentor developers, define architecture, maintain CI/CD pipelines, and collaborate cross-functionally to deliver production-ready solutions.

The role involves designing and building AI/ML products, collaborating with teams to enhance models, optimizing performance, and staying updated on Gen AI advancements.

The Forward Deployed Engineer guides customers in AI transformation through technical validation, solution architecture, and implementation, ensuring customer success and innovation.

Lead deployment and ongoing success of Prosper's AI agents across healthcare customers. Own portfolio performance, commercial outcomes, metrics, expansion, and ARR. Diagnose issues from data and interactions, drive engineering and ops improvements, build playbooks, and manage executive client relationships to scale deployments.

The Applied AI Engineer will work with language models to create applications, manage the model alpha program with OpenAI, conduct experiments, and influence policies.

The AI Architect Lead will strategize and assess AI technologies, collaborate with stakeholders, conduct PoCs, and establish AI frameworks while ensuring compliance and risk management.

Lead product vision, strategy, and roadmap for DataHub's catalog products. Research user needs and market, translate ambiguous problems into scoped initiatives, drive cross-functional launches, create feedback loops with customers, and deliver measurable business and customer outcomes.

The AI Engineer will design and deliver AI systems, focusing on LLM and Generative AI solutions, integrating models, and ensuring compliance with security constraints.

As a Research Manager at CBAI, you will support fellows in AI and biosecurity research by providing feedback, managing projects, and facilitating resource connections. You will also contribute to program management and development, ensuring fellows succeed in their research goals.

Own AI feature delivery across multiple internal platforms: write detailed AI-native user stories and acceptance criteria, prioritize backlog, lead grooming and sprint planning, perform acceptance testing/QA, triage AI service-desk requests within SLA, liaise with engineering and senior stakeholders, and produce documentation and process flows to support deployment and adoption.