AI Jobs in Los Angeles

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Contributors may generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

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Contributors generate prompts that challenge AI, evaluate AI-generated solutions for correctness, assumptions, and logic, improve AI reasoning to align with first principles and accepted standards, and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may generate prompts that challenge AI; evaluate AI-generated solutions for correctness, assumptions, and logic; improve AI reasoning to align with first principles and accepted standards; and apply structured scoring criteria to assess multi-step problem solving.

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Contributors may design original optics problems that simulate real physics research workflows, ensure problems are computationally intensive and cannot be solved manually within reasonable timeframes, develop problems requiring non-trivial reasoning chains in mechanics, electromagnetism, thermodynamics, and quantum mechanics, base problems on real research challenges or practical applications from optics and physics practice, and document problem statements clearly with verified correct answers.

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As a Staff/Principal-level Technical Lead, you will be responsible for driving the end-to-end technical execution of multiple concurrent enterprise engagements in close partnership with the Project Lead, from technical discovery to production deployment. You will architect and implement secure, highly scalable integrations between the AI platform and clients' existing data pipelines, APIs, and infrastructure. You will lead technical discovery sessions, architecture workshops, and data readiness assessments with customer IT, data, and engineering leadership teams. You will build and customize AI-enabled solutions, scripts, and workflows that address complex business problems identified in the sales process. You will serve as the primary technical liaison and escalation point between customer engineering teams and internal product, engineering, and data science teams to unblock deployments quickly. You will ensure that all deployed solutions meet enterprise-grade standards for performance, security, data privacy, and scalability. You will debug complex integration issues, manage technical risks across overlapping projects, and provide hands-on troubleshooting during implementation. Additionally, you will contribute to the internal codebase by documenting technical blueprints, developing reusable integration components, and providing product feedback based on real-world edge cases.

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Design agent systems from first principles by deciding the loop, tools, context strategy, and evaluation method, choosing between different architectural approaches and defending choices with data; engineer the context including prompt design, context windows, tool surfaces, structured outputs, and citation grounding; drive evaluation rigor by building evaluations prior to agent development, diagnosing failures, fixing root causes, and proving improvements; use AI tools such as Claude Code and Codex extensively for planning, scaffolding, refactoring, and debugging; and become a domain expert in healthcare claims, coding guidelines, and medical records to contribute effectively.

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Design agent systems from first principles including deciding the loop, tools, context strategy, and evaluation. Choose between single-agent and multi-agent topologies, LLM reasoning and deterministic post-passes, and retrieval versus direct context loading, defending these choices with data. Engineer the context by focusing on prompt components such as context windows, tool surfaces, structured outputs, citation grounding, and the prompt itself. Drive evaluation rigor by building evaluations prior to agent development, diagnosing failures, fixing root causes, and proving metric improvements. Use AI tooling extensively for planning, scaffolding, refactoring, and debugging your work. Become a domain expert in healthcare claims, coding guidelines, and medical records to contribute significantly within the domain.

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Own the implementation of AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading regular demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Set up SIP/CCaaS/PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Write and refine prompts for LLM-driven agents, monitor performance, test iteratively, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions and work consultatively to unblock challenges in security, connectivity, or knowledge ingestion. Collaborate with product and engineering teams to escalate platform gaps and resolve technical fixes and platform issues while independently driving leading client implementations.