Top AI & Machine Learning Jobs

Develops statistical, machine learning, predictive, and advanced analytical solutions using structured and unstructured public health data. Integrates and analyzes data from surveillance, research, and administrative sources; evaluates data quality; creates visualizations and reports; monitors analytical tools; and translates findings into actionable public health insights. Collaborates with multidisciplinary CDC program teams and communicates technical results to technical and non-technical audiences.

Design and evaluate AI-enhanced instructional materials and learning experiences, lead Agentic AI implementations, collaborate with academic and technical teams, assess educational effectiveness, and train faculty and staff on AI-enabled teaching practices. The role requires instructional design expertise, higher education experience, knowledge of ethical AI and data privacy, familiarity with adaptive learning and data analytics, and a portfolio or published work demonstrating AI in education.

Leads Mayo Clinic’s enterprise AI and robotics strategy, governance, portfolio prioritization, operational enablement, and workforce adoption. The role develops multi-year roadmaps, establishes responsible AI and interoperability standards, partners with clinical, technical, legal, facilities, and security stakeholders, and oversees procurement and vendor governance. As a people leader, the Director manages operations managers, execution processes, reporting, budgets, and cross-functional initiatives while advising senior executives on AI strategy and value realization.

Build production-ready agentic AI applications that solve customer problems. Design workflows, plugins, and tool integrations; translate ambiguous needs into intuitive AI experiences; rapidly iterate applications into production; integrate APIs and enterprise systems; and partner with platform and product engineers. Own customer-facing quality, including user experience, reliability, performance, and evaluation, while identifying reusable patterns across deployments.

Lead product strategy and execution for conversation intelligence, voice, and dialing. Deliver AI-native experiences, ship AI/ML features, run discovery with customers, and manage a team of product managers to drive real-time coaching, deal acceleration, and leader-facing analytics across a B2B SaaS revenue platform.

Leads the data science function, setting strategy, technical direction, governance, and machine learning standards. Partners with senior leaders to prioritize analytics initiatives across acquisition, retention, customer experience, pricing, and portfolio performance. Personally develops and validates advanced models while leading production deployment, monitoring, and continuous improvement. Manages, mentors, and grows a team of data scientists and machine learning engineers, ensuring reliable, explainable, and business-impactful decision systems.

Own end-to-end development of an AI-native, agentic enterprise platform. Build reliable agent systems, scope products from user needs, ship backend-heavy full-stack features, review code, and drive AI-first development. The role requires strong product sense, comfort with ambiguity, deep Go expertise, and experience building AI products or systems from scratch.

Implement and optimize AI Operations and observability capabilities for event correlation, anomaly detection, alert-noise reduction, incident response automation, and operational analytics. Configure monitoring and ITSM integrations, develop automated remediation workflows, evaluate AI/ML operational use cases, and support SRE and service-management teams. The role also provides technical guidance, documents best practices, and assesses emerging AIOps technologies and platform enhancements.

Senior Software Engineer to design and implement C++/Python software, accelerate workloads with CUDA, develop and maintain Linux kernel modules and system services, optimize system and GPU performance, manage Linux systems, mentor junior engineers, and integrate AI technologies across products.

Develops and evaluates machine learning and deep learning models using biomedical imaging, genomic, transcriptomic, clinical, and text datasets. Builds data pipelines and computational tools, performs feature engineering and validation, conducts cancer genomics research, and contributes reproducible code. Collaborates with computational biologists, clinicians, engineers, and external partners to identify biomarkers and generate scientific insights. Communicates findings through reports, presentations, abstracts, and manuscripts.