Top AI Engineer Jobs in Los Angeles, CA

As a Sr. Applied AI Engineer at Zapier, you will build and enhance AI platform capabilities, focusing on LLM Ops and ML Ops to support scalable AI development across teams.

Design, develop, and maintain generative AI and cloud-based AI solutions using Microsoft Azure and Power Platform tools. Collaborate with cross-functional teams to gather requirements, build scalable AI applications, deploy and manage models with Azure Machine Learning, test/debug, document designs, and provide training and user support.

Design and build LLM-powered agents for hardware product development. Responsibilities include ensuring effective coordination among engineering disciplines and integrating AI systems with existing tools.

As a Staff Applied AI Scientist, you will design and deploy AI systems using LLMs, improve agent performance, and collaborate with engineering to enhance systems for customer impact.

As a Senior AI/Data Engineer, you will design and maintain scalable data and AI infrastructures, partnering with cross-functional teams to deliver reliable data systems and support AI-driven business capabilities.

Join Capsule as an AI Engineer to develop ML models for video editing. Responsibilities include researching ML techniques, collaborating on feature development, and optimizing model performance.

Design, prototype, and ship RAG, agentic AI, and LLM solutions from concept to production. Integrate fine-tuned models into microservices, implement monitoring/testing, collaborate on data preparation, and communicate results to stakeholders while supporting rapid prototyping and continuous improvement.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Build and productionize agentic workflows and RAG pipelines, integrate LLMs into the GTM stack, and develop high-performance backend services and ML pipelines. Connect quoting engine, Salesforce, and data lakes, solve synchronization and architectural challenges, and ensure observability, scalability, and reliability for sales automation tools.

The Staff AI Engineer will design and deploy production-level AI systems, guiding architectural decisions and collaborating with teams to build AI features at scale.

As an Applied AI Engineer, you'll build AI agents and experiences from concept to production, collaborating across teams and optimizing systems for usability and quality.

Lead architecture and technical strategy for AI-driven product quality systems using LLMs and agents. Build scalable evaluation frameworks, detect regressions, generate insights, and drive cross-functional adoption while mentoring engineers and defining standards for trustworthy AI.

Lead design and implementation of production agentic AI systems that integrate LLMs with Teak's platform APIs. Build RAG pipelines, MCP/tool integrations, evaluation frameworks, and AI infrastructure on cloud (AWS/GCP/Azure). Contribute backend services in Python, ensure compliance and safety, and operate systems in production including observability and on-call duties.

As a Staff Backend AI Engineer, you will design and operate backend AI systems for Arbiter's care orchestration platform, ensuring reliability, low latency, and scalability while collaborating across teams and mentoring junior engineers.

Lead design, deployment, and monitoring of LLM-based AI solutions. Build end-to-end generative AI applications, fine-tune models, integrate LLM APIs into products, ensure post-deployment performance, and collaborate cross-functionally within an Agile environment to drive measurable business impact in healthcare.

The Senior AI Engineer will design and maintain customer-facing AI tools by building APIs, optimizing workflows, and conducting experiments with generative AI models, ensuring system reliability and integration with production systems.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Design, build, and operate Jellyfish's Databricks Lakehouse and data/AI platform. Build ingestion, transformation, and delivery pipelines; govern data with Unity Catalog; enable BI, agentic analytics, metadata, lineage, and observability. Provide technical leadership, set architecture and standards, mentor peers, and collaborate with cross-functional stakeholders to make Databricks the foundation for analytics and AI.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

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.

The Staff AI Engineer will drive architecture and implementation of AI-assisted engineering practices, overseeing technical standards, automated testing, and code review systems for software development in a regulated fintech environment.

The AI Engineer will develop scalable AI systems, automate workflows, and collaborate with business teams to enhance operational efficiency across OpenSesame.

The AI Engineer will design, develop, and deploy AI-powered systems, improve operations with intelligent workflows, and collaborate on high-leverage AI opportunities.

The AI Developer will design, implement, and deploy AI agents and workflows, integrating advanced models and ensuring reliable performance in production environments.

The Full-Stack Applied AI Engineer will design and build AI-enabled products, own delivery from UI to backend, and implement robust systems with a focus on security and reliability, collaborating closely with clients and team members.