Top AI Jobs in Los Angeles, CA

Lead development and operationalization of a clinical reasoning framework for trustworthy clinical AI. Partner with product, engineering, editorial, and design to define decision pathways, evaluation benchmarks, validation protocols, and ongoing expert review. Translate clinical feedback into AI system improvements, represent the team externally, and optionally maintain limited clinical practice.

Build and operate Coinbase's LLM and agent platform: unified model gateway, AI Hub, secure microVM agent runtime, knowledge base integrations, AI FinOps, observability and evaluation tooling, and production-grade agent deployments with teams across the company.

Lead portfolio-level Data & AI strategy and technical governance for enterprise clients. Advise C-suite, drive architecture and modern data stacks, win high-value engagements, and scale engineering and data science teams.

The Enterprise Architect for Data & AI will shape the architecture for the SaaS platform, ensuring efficient data practices and responsible AI governance, while collaborating with various teams to align technology with business goals.

Lead data engineering for big data and AI initiatives. Architect and optimize scalable data pipelines, integrate AI technologies, collaborate with stakeholders, and mentor engineering teams.

Lead data-science projects with customers to develop cloud-based, scalable models and data pipelines. Acquire and prepare data, evaluate and improve models, write extensible code, and collaborate across engineering, product, and partner teams to deliver high-impact AI solutions and best practices.

No responsibilities were provided in the job posting.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

The Principal AI/ML Engineer will lead the development of AI/ML platforms, drive technology architecture, ensure quality and integration, and provide strategic leadership while managing a team of engineers.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Lead and build Deepgram's AI enablement function: prototype and ship reusable agents, MCP servers, paved-road workflows, prompt and retrieval patterns, and an enablement hub. Drive company-wide AI adoption with measurable productivity metrics, embed safe-use guardrails, partner across Platform, Security, Data, and People Ops, and grow a central team and distributed champions network.

Lead end-to-end delivery of ML infrastructure and AI tooling programs, defining architecture and rollout strategies for training, serving, and monitoring. Coordinate research, engineering, product, and data teams to optimize inference cost/latency, build internal tools for experiment tracking and model versioning, and resolve bottlenecks to enable faster, scalable ML deployments.

Lead product vision and roadmap for agentic AI across GTM applications. Partner with engineering, data, and business teams to identify AI opportunities in Lead-to-Cash and Customer Success, define epics/user stories, prioritize backlog, set AI KPIs, and drive adoption of LLMs, autonomous agents, and responsible AI practices to deliver automation-first GTM workflows.

Design, build, and operate agentic AI systems end-to-end for Professional Services. Translate stakeholder needs into secure, scalable multi-agent architectures, define performance benchmarks, manage deployments and incident response, advise leadership, mentor teammates, and maintain runbooks and production tooling including on-call responsibilities.

Lead and deliver large, high-risk HPC and AI deployments across the Americas. Manage customer relationships, project financials and P&L, cross-functional teams, schedules, legal/commercial issues, and on-site acceptance. Mentor team members and provide performance input while coordinating internal and external resources.

Design, implement, and validate scalable AI infrastructure on Azure. Review and test code, debug using telemetry, coordinate dependencies and partner teams, ensure scalability, performance, and live-site readiness for production services.

Lead and scale GM's enterprise Privacy and AI governance operations: build workflows, training, metrics, reporting, and documentation; operationalize AI impact assessments and governance platform; drive continuous improvement, automation, and cross-functional stakeholder alignment to strengthen privacy and responsible AI program maturity.

Curate AI news by selecting important stories, summarizing them, and delivering concise content to newsletter subscribers, spending roughly an hour daily.

The role involves leading AI initiatives, defining strategic roadmaps, managing teams, and ensuring compliance while collaborating with various stakeholders.

The Solutions Architect will lead the VAST-GCP alliance, designing solutions and collaborating with teams to deliver joint offerings for AI/ML and enterprise workloads.

The role involves designing and delivering AI training programs, creating learning materials, and facilitating workshops to enhance user understanding of AI tools within enterprises.

As a Senior AI Architect, you'll design scalable AI capabilities within a SaaS platform, focusing on architecture, implementation quality, and integration with modern technologies, ensuring operational robustness in production environments.

The Senior AI Data Engineer will design and build scalable data pipelines, ensure data quality, and support AI product development through a unified analytical data framework.

The Software Architect will lead AI quality evaluation, design evaluation frameworks, create risk controls, and ensure compliance, partnering with various teams on AI systems.

As a Senior ML Engineer, you'll own ML-driven features from modeling to deployment, drive innovation in generative AI, and mentor junior members.