LLMs AI Jobs

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Build the analytical foundation to evaluate compensation competitiveness by connecting offer data, band position, acceptance rates, and market benchmarks into a live system that recommends specific adjustments. Develop predictive models and tooling to help managers and recruiters make better and faster decisions, such as a regretted attrition model that flags at-risk employees 90 days in advance. Design and deploy AI agents that draft first-pass recommendations for high-stakes people decisions including compensation, promotion, and hiring, which are then reviewed and adjusted by people leaders. Build recruiting analytics to connect sourcing channels, time-to-hire, first-year performance, and tenure to reallocate recruiting spend and provide weekly insights to recruiting leadership. Analyze organizational effectiveness by examining spans and layers, talent density, and hiring efficiency to identify structural inefficiencies. Partner with finance to transition from spreadsheets to a live workforce model accounting for attrition, hiring velocity, and ramp time by function. Use LLMs and agentic workflows to analyze unstructured people data at scale, including support tickets, exit interviews, performance reviews, and engagement survey responses. Replace recurring reporting cycles with always-on agents that surface insights to leaders as needed. Support high-stakes organizational and talent decisions with rigorous analysis, including executive hiring, retention, and reorganizations.

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Architect the AI-native codebase by redesigning the structure of a fast-moving, AI-generated codebase to bring scalability, maintainability, and long-term thinking, and make it production-ready. Exercise strong technical judgment by making smart, opinionated decisions about architecture, tooling, and trade-offs, and leading the team in these decisions. Build full-stack features end to end, owning development from front-end (React) to back-end (Node.js, Django, Python), shipping prototypes to production quickly and ensuring quality follows. Work hands-on with large language models (LLMs), AI agents, tool calling, and memory systems to solve real product challenges as a core responsibility. Collaborate with product and marketing teams to shape the product by defining what gets built, focusing on users and outcomes, not just code.

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Serve as the technical point of contact for healthcare customers, translating ambiguous problems into scalable solutions. Build and deploy AI systems, including RAG pipelines and multi-agent workflows, and develop real-time conversational AI systems. Work with healthcare data integration standards such as HL7 v2, FHIR, REST APIs, and X12, and build pipelines using Python and SQL. Own the full lifecycle of the AI solutions from design to monitoring, including troubleshooting production issues. Collaborate with product, AI research, and clinical teams. Operate onsite at customer locations in Minneapolis, MN, with occasional travel to Hippocratic AI headquarters for strategic planning and team sessions.

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Own end-to-end implementation of AI-driven detection features, from discovery to production deployment and iteration. Design and build data enrichment pipelines to extract structured information from messy, real-world financial transaction data. Research fraud and scam typologies relevant to older adults and translate findings into scalable detection logic. Build evaluation frameworks including metrics, error analysis, and model comparisons to measure system performance and drive improvement. Optimize AI pipelines for accuracy, latency, and cost by making informed tradeoffs on model selection and architecture. Collaborate with Customer Service, Go-to-Market, and partner-facing teams to ensure solutions meet real-world needs and deliver measurable impact. Stay current with developments in LLMs, agent architectures, and applied AI to identify practical applications for the domain.

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This leader will build, scale, and manage OpenAI's User Operations Strategy & Operations function, owning service strategy, planning, forecasting, and execution. They are responsible for embedding AI-native operations, forecasting support needs, evolving support models, driving operational quality, and acting as a senior escalation point across the team.

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The AI Outcomes Manager will collaborate with executives and end-users to develop and execute AI transformation strategies using Glean's platform. They will conduct discovery workshops, drive customer engagement, and work closely with Product and R&D to shape product direction based on user feedback.

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You will review and evaluate complex AI agent tasks and scenarios for logic, completeness, and realism, identifying inconsistencies and defining expected behaviors. Collaboration with QA, writers, and developers is essential to suggest task refinements and cover edge cases, ensuring comprehensive agent testing.

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You will review evaluation tasks and scenarios for logic, completeness, and realism, identify inconsistencies or missing assumptions, and help define gold standards for autonomous AI agents. Collaborate with QA, writers, and developers to refine AI agent testing and ensure comprehensive evaluation frameworks.

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The Analyst will review evaluation tasks and scenarios for logic, completeness, and realism while identifying inconsistencies and helping define gold standards for AI agents. They will closely collaborate with QA, writers, or developers to refine processes and ensure robust agent testing.

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Review evaluation tasks, scenarios, and policies for autonomous AI agents focused on logic, completeness, and realism. Annotate reasoning paths, identify inconsistencies, and help define expected behaviors while collaborating with QA, writers, or developers for scenario refinement.