Top AI & Machine Learning Jobs

As a Staff Applied ML Scientist, you will develop and maintain fraud detection models, conduct research on fraud types, and collaborate across teams to drive product development and real-time decision-making.

Design, prototype, and deploy LLM-powered agents, RAG workflows, and automations that accelerate internal teams and customer-facing product features. Integrate AI systems with SaaS tools, build reusable components and evaluation frameworks, embed with business teams to deliver measurable outcomes, and establish standards for responsible AI, monitoring, and cost control across the company.

The Senior AI Product Engineer will develop and optimize AI product features, ensuring a seamless user experience through collaboration with engineering teams. Responsibilities include building full-stack features, implementing user feedback mechanisms, and monitoring product performance.

Design, build, and own production AI agents and automations for marketing. Integrate and instrument the marketing stack (Salesforce, HubSpot, Qualified, Clay, ZoomInfo, data warehouse), implement predictive scoring and personalization, monitor systems for reliability, and collaborate with marketing, RevOps, and sales to translate problems into deployable software.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

Lead and grow a distributed engineering team to build and scale AI platforms and agents for healthcare. Own technical strategy, data/retrieval/semantic architectures, ML model serving and MLOps, and deliver HIPAA-compliant, cloud-native systems in partnership with Product, Data Science, and Clinical teams.

Lead development of AI-powered patient experiences: LLM-driven intake and therapist matching, between-session support, evaluation and safety infrastructure, outcome measurement, and HIPAA-compliant product delivery. Partner with Engineering, ML, Data Science, Clinical, and Legal to ship and monitor models in production, write evals, design red-teaming and human-in-the-loop flows, and measure patient outcomes.

Lead analytics strategy and decision support, perform deep-dive analyses, build predictive and ML models, design experiments, create trusted reporting and dashboards, ensure data quality, partner cross-functionally, and influence product, growth, revenue, operations, and finance through evidence-based recommendations.

Administer, secure, and optimize enterprise AI tools and LLM platforms. Manage access, SSO integrations, usage policies, cost monitoring, audits, and SOC 2 readiness. Build automation scripts, train employees, and partner with IT, Security, and Business teams to enforce AI governance and data protection.

Partner with business teams to identify high-leverage AI opportunities; design, build, and ship agentic AI solutions and internal AI platform components. Deliver production LLM-based agents, integrations, and CI/CD pipelines while driving AI enablement, safety, and adoption across the company.