Top AI & Machine Learning Jobs

Lead Grow's AI Enablement team to define and execute the company-wide AI roadmap. Build and operate core AI infrastructure (data pipelines, embeddings, retrieval, model access, connectors, and governance) with PHI compliance. Architect integrations and APIs enabling agentic workflows, drive delivery of high-priority LLM and AI-assisted care use cases, and manage vendor governance, procurement, and cross-functional alignment.

As a Machine Learning Engineer in Fraud Risk, you'll build and maintain ML systems for fraud detection and anomaly analysis, ensuring robust performance and real-time decision making.

Lead architecture and technical vision for integrations that ingest external data (e.g., Salesforce, HubSpot) into Jasper's IQ layer. Design scalable, secure data ingestion, define context primitives, drive cross-team alignment, and deliver staff-level initiatives that improve reliability and AI-driven product quality.

Intern will support BizOps-led AI transformation: build and maintain an internal AI knowledge layer, manage knowledge and documentation, create executive-ready decks and financial models, run AI enablement, coordinate cross-functional initiatives, and measure AI adoption and impact.

Lead AI Observability product from vision through GA and post-GA growth. Own roadmap, business outcomes (adoption, retention, expansion), pricing, and exec reporting. Drive cross-functional engineering and design teams, partner with enterprise customers on production AI/LLM issues, build agentic AI prototypes, and iterate on features and packaging based on GA customer feedback.

Lead AI-driven strategic projects across growth and case automation: translate workflows into executable plans, prioritize opportunities with product and engineering, build measurement frameworks, align cross-functional teams, and interface with senior stakeholders.

Oversee client data pipelines, automated ETL/ELT workflows, data quality, and governance within a GCP environment. Lead deployment and scaling of Media Mix Models, causal impact analyses, and market testing frameworks. Manage data engineering teams, guide technical product roadmaps, collaborate with media analytics and marketing teams, and present data science methodologies and insights to client stakeholders.

Lead deployment and scaling of advanced measurement (MMM, causal methods, market tests), drive AI/product roadmap for GenAI agents and automation in GCP/Gemini/ADK, translate technical methods for clients, and coordinate media and data teams to operationalize modeling insights.

Lead marketing science and GenAI initiatives: deploy and scale MMM, causal impact, and testing frameworks; define AI/product roadmap within GCP/Gemini/ADK; collaborate with media analytics and present technical work to clients.

Drives adoption and measurable business value of AI and machine learning models across Gap Inc. brands. Responsibilities include managing model adoption lifecycles, creating explainability materials, coordinating A/B tests, building dashboards, measuring business impact, diagnosing adoption barriers, and advising senior stakeholders. The role partners with merchandising, planning, inventory, sourcing, data science, and engineering teams to integrate models into workflows, train users, and improve human-AI collaboration.

Own end-to-end fullstack development for a healthcare recommendation platform: build web and mobile UIs, backend APIs, integrate AI-driven conversational features, manage AWS deployments, secure data systems, and iterate using metrics and A/B testing in a monorepo environment.

Lead vision and execution for agentic AI products that reason, plan, and act across apps and the web. Define MVPs from model capabilities, collaborate with research/engineering/design, set up data and evaluation processes, balance infrastructure trade-offs, and drive adoption and continuous improvement for scalable generative AI experiences.

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.

Design, implement, and optimize distributed pre-training infrastructure for large-scale GPU clusters. Profile, benchmark, and debug compute, memory, networking, and storage bottlenecks; optimize communication libraries and GPU programming stacks; collaborate with hardware and model teams to scale training, ensure reliability, and influence architecture and roadmap.