About The Position
At Oracle Analytics, we are building the next generation of enterprise AI products to enable intelligent data analysis at scale. Leveraging our foundational strengths in data management and enterprise software applications, we are advancing our platforms and applications by deeply embedding cutting-edge agentic AI, generative AI, and innovations in machine learning and optimization. Our AI and Applied Science team is seeking a highly motivated Senior Research Engineer, for our AI Data platform to lead innovation in agentic systems, efficient LLM modeling, and human-in-the-loop experiences. In this role you will research, architect, and prototype next‑generation agents (tool use, planning, orchestration) and efficiency techniques (distillation, quantization, retrieval, caching) that improve latency, cost, and quality for enterprise workloads. You will also design and build rich, interactive UIs to enable human oversight, feedback, and troubleshooting—closing the loop between model behavior, evaluation, and product impact. Partnering closely with applied scientists, research engineers, and product teams, you will take your solutions from lab to production, driving measurable outcomes and shipping globally scaled intelligent applications. This role requires a solid foundation in agentic and generative AI coupled with deep expertise in building rich, production-grade interactive UIs for human-in-the-loop workflows. You will design novel solutions that turn business requirements into intuitive experiences for review, feedback, and oversight—spanning prompt/tool orchestration consoles, evaluation dashboards, trace/telemetry views, safety and guardrail configuration, and annotation/feedback loops. Demonstrated experience taking science prototypes to production is essential, including integrating LLM backends (streaming, caching, retrieval, tool use), instrumenting rigorous evaluation (offline metrics, A/B testing, rubrics), and ensuring reliability, performance, accessibility, and security. Hands-on proficiency with modern web stacks (e.g., TypeScript, React, design systems, data visualization), real-time collaboration, and observability is a must; familiarity with LLM post-training/fine-tuning, alignment, and agent orchestration is strongly preferred to partner effectively with modeling teams. Experience visualizing and interacting with structured, tabular, graph, or time-series data is a strong plus. We’re looking for candidates who thrive in fast-paced environments, reduce ambiguity through prototyping and user testing, and own outcomes end to end—from concept to globally scaled deployment.
Requirements
- Solid foundation in agentic and generative AI
- Deep expertise in building rich, production-grade interactive UIs for human-in-the-loop workflows.
- Demonstrated experience taking science prototypes to production is essential
- Hands-on proficiency with modern web stacks (e.g., TypeScript, React, design systems, data visualization), real-time collaboration, and observability is a must
Nice To Haves
- Familiarity with LLM post-training/fine-tuning, alignment, and agent orchestration is strongly preferred to partner effectively with modeling teams.
- Experience visualizing and interacting with structured, tabular, graph, or time-series data is a strong plus.
- Thrive in fast-paced environments, reduce ambiguity through prototyping and user testing, and own outcomes end to end—from concept to globally scaled deployment.
Responsibilities
- Research, architect, and prototype next‑generation agents (tool use, planning, orchestration)
- Design and build rich, interactive UIs to enable human oversight, feedback, and troubleshooting
- Partnering closely with applied scientists, research engineers, and product teams, you will take your solutions from lab to production, driving measurable outcomes and shipping globally scaled intelligent applications.
- Design novel solutions that turn business requirements into intuitive experiences for review, feedback, and oversight—spanning prompt/tool orchestration consoles, evaluation dashboards, trace/telemetry views, safety and guardrail configuration, and annotation/feedback loops.
- Integrating LLM backends (streaming, caching, retrieval, tool use), instrumenting rigorous evaluation (offline metrics, A/B testing, rubrics), and ensuring reliability, performance, accessibility, and security.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
