Top AI Jobs in Los Angeles, CA

Lead strategy and execution for generative and multimodal vision systems, from research to production. Design model architectures, adaptation, evaluation, and deployment pipelines; improve realism, controllability, robustness, and temporal consistency; benchmark and guide model selection; mentor scientists and coordinate cross-functional engineering to ship production-ready AI capabilities.

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.

Lead client engagements to assess business challenges, define enterprise data and AI strategies, develop roadmaps, guide solution design and delivery, provide technical leadership and mentoring, and support practice growth via presales and thought leadership.

Lead and scale AI platform and agent engineering teams to build AI infrastructure, agent runtimes, MLOps/LLMOps, prompt and skills lifecycle management, and forward-deployed engineering for healthcare customers. Drive platform reliability, security, observability, enterprise integrations, and operational excellence to deploy AI-powered solutions at scale.

Build, deploy, and maintain AI-powered agents, APIs, and applications for People operations on Snowflake. Turn messy business requirements into production systems, integrate with Workday/Notion/case tools, ensure data governance and security, design multi-model LLM reliability controls, and operate full lifecycle including CI/CD, monitoring, and incident fixes while partnering with non-technical stakeholders.

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.

Design and deliver custom AI-driven legal workflows, implement and scale post-sales solutions, define KPIs and success metrics, inform product strategy, partner with Customer Success to demonstrate ROI, and maintain objective, ethics-compliant processes and playbooks for plaintiff law firms.

Lead a team to develop groundbreaking technology combining NLP, big data, and AI to enhance digital communication and interaction.

The Interaction Intelligence Program Manager leads the deployment of AI capabilities in the contact center, overseeing initiatives, technical oversight, and change management with a focus on ethical AI and stakeholder collaboration.

Lead discovery and delivery of AI-first AEC product capabilities. Define roadmap, prototype agentic LLM workflows, translate research into requirements, measure outcomes, and partner with UX, Engineering, and GTM to drive adoption and growth.

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.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Lead product management in AI-driven safety solutions, focusing on video-based safety products, managing a roadmap and collaborating across teams for customer impact.

The Senior GTM Engineer in AI Innovation will strategize and implement AI initiatives across Marketing, Sales, and Customer Success while also leading cross-functional efforts and managing a growing team.

Teach in-person Applied AI course using CodePath curriculum for ~25-75 students. Prepare and deliver engaging lessons on generative models and LLMs, coordinate with program manager, co-instructors, and TAs, implement curriculum improvements based on feedback, and hold weekly TA meetings during the Fall term (Aug–Dec).

Evaluate and author high-quality computer science content to improve AI model reasoning and accuracy. Analyze complex CS problems, create scenarios/datasets, review AI outputs for correctness, and collaborate remotely to refine guidelines and documentation.

As the Senior Data Scientist for AI & Model Risk, you will lead AI risk assessments, coordinate with stakeholders, evaluate AI system designs, and ensure compliance with risk management principles in financial services.

Embed with teams to audit workflows, build and productionize LLM/agent automations (using Claude and integrations), create reusable assets and guardrails, coach teams to self-sufficiency, and measure outcomes to prioritize future engagements.

Lead design, deployment, and operationalization of agentic LLM-driven workflows across GTM systems. Translate business needs into scalable AI integrations, define orchestration and governance patterns, guide engineering teams, ensure security/compliance, and drive stakeholder alignment for enterprise AI programs.

Lead hands-on engineering delivery of agentic AI solutions for GTM systems. Design and build LLM-powered workflows, autonomous agents, RAG and semantic search pipelines, Salesforce and Slack integrations, CI/CD and observability, enforce AI governance/security, mentor engineers, and drive prototypes to production across enterprise GTM platforms.

Design, build, test, deploy, monitor, and maintain production LLM-based agentic solutions for Commercial Customer Success. Integrate agents with CRMs and Samsara systems, define evaluation frameworks and success metrics, implement automated tests and monitoring, iterate on models and prompts, and demonstrate impact on customer outcomes and retention.

Integrate and fine-tune existing AI models into product features, build evaluation frameworks and observability for production AI, collaborate with data science and engineering, and develop AI-powered application features using Python to improve user experience.

Build and operate an enterprise AI platform and integrations that connect LLMs to internal systems. Develop production AI applications, manage model/provider evaluations, ensure security/compliance for government environments, and deliver company-wide AI training and enablement.

Build and maintain AI platform infrastructure, connectors, API integrations, and custom AI-powered applications. Implement authentication, data pipelines, and tooling to embed LLM capabilities across teams. Contribute code, documentation, reviews, and enablement while ensuring security and compliance for government requirements.

Build and lead an enterprise AI platform: design infrastructure, integrations, and custom AI applications. Own provider/tool selection, deploy production model pipelines and connectors, ensure compliance with government security requirements, mentor engineers, and deliver company-wide AI enablement and training.