Top AI & Machine Learning Jobs

Build AI workflows, agents, and customer-facing web experiences that accelerate website production and improve conversion. The role involves optimizing Contentful-based processes, configuring chat, enrichment, voice AI, and personalization tools, running experiments, establishing AI guardrails, monitoring performance, and training marketing teams on reusable AI systems. Collaboration spans Web Platform, CRO, SEO, product marketing, legal, privacy, and brand teams.

Leads the technical direction, architecture, and roadmap for agentic machine learning systems processing global trade data. Builds ingestion, document understanding, entity resolution, inference, evaluation, observability, and feedback systems. Adapts and post-trains LLMs, ships production AI agents, mentors engineers, sets technical standards, and partners with product, engineering, data science, and customers to deliver risk, compliance, and enforcement capabilities.

Design and deploy production-grade ML models and GenAI/LLM solutions to improve customer segmentation, churn prediction, LTV estimation, and automate enterprise workflows. Build cloud-native data science pipelines, measure impact via experiments, create executive dashboards, and mentor peers on MLOps and data storytelling.

Leads advanced data science projects involving machine learning, deep learning, statistical modeling, forecasting, segmentation, search, recommendations, ranking, and generative AI. Develops production-grade code and evaluation frameworks, deploys scalable models, collaborates with business and technical teams, communicates insights, and mentors junior data scientists.

Design and build MLOps infrastructure for production deployment of data, machine-learning models, and software. Develop end-to-end ML pipelines, CI/CD/CT workflows, secure repository strategies, containerized deployments, and automation for Edge AI applications. Collaborate with developers, data scientists, and ML engineers to define tooling, monitor DevOps performance, and improve operational efficiency. Support embedded-system CI/CT, open-source pipelines, and model testing.

Build and maintain production LLM agent pipelines, MCP integrations, and RAG systems using Python, FastAPI, LangChain, TypeScript, and React. Use Claude Code and other AI-assisted tools daily for development, debugging, and iteration. Collaborate on software optimization, code reviews, root-cause analysis, architecture improvements, testing, documentation, scalability, reliability, and security. Translate requirements into production-ready applications and support planning through user stories and estimates.

Develops advanced analytics and machine learning models for client business challenges, particularly using pharmaceutical and healthcare data. Translates complex analyses into actionable business recommendations, communicates findings to clients, manages analytical workstreams, supports project milestones, builds client relationships, and mentors junior team members.

Architect and own marketing data infrastructure, including Databricks pipelines, Gold Layer tables, semantic models, and documentation for self-service analytics. Lead cross-functional data initiatives, ensure reliability and quality, mentor engineers, and build production LLM and agent systems. Translate ambiguous business needs into scalable solutions, automate marketing and go-to-market workflows with AI, and deliver high-quality Python and SQL while overseeing AI-generated code.

Annotate and categorize large image and data datasets for machine learning model training. Follow detailed labeling guidelines, perform quality-control checks, collaborate with stakeholders, and improve annotation workflows to increase accuracy and efficiency. The role requires sustained attention to detail during repetitive, high-volume work and is fully onsite in Santa Ana, California.

Build and deploy predictive models, recommendation systems, LLM applications, incentive optimization frameworks, and causal inference models for customer growth. Partner cross-functionally to improve acquisition, engagement, retention, and expansion. Measure marketing impact, guide investment decisions, develop AI analytics and decision systems, and provide technical leadership and mentorship across data science initiatives.