Senior Machine Learning Engineer, Agentic AI

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

• →Lead the design and evolution of agentic AI systems that power intelligent customer experiences across Robinhood.
• →Define the technical direction for evaluating autonomous agents, including reasoning quality, planning, tool selection, memory, task completion, safety, latency, and overall user experience.
• →Design and build scalable evaluation frameworks for agentic systems using automated evals, benchmark datasets, LLM-as-a-Judge techniques, and human feedback to continuously improve agent performance.
• →Drive model selection and optimization across frontier foundation models, fine-tuned models, retrieval systems, and tool-using agents, balancing quality, latency, cost, and reliability.
• →Partner closely with Product, Data Science, and Engineering to establish launch criteria, quality standards, and measurable success metrics for production agentic systems.
• →Improve agent reliability by investigating production failures, identifying root causes across reasoning, planning, retrieval, and tool execution, and driving architectural improvements.
• →Mentor engineers and influence technical direction across teams while helping establish best practices for building reliable, production-ready agentic AI systems.

• Significant experience building and deploying production AI systems powered by large language models, autonomous agents, or multi-step reasoning workflows.
• Deep understanding of modern agent architectures, including tool calling, planning, memory, retrieval-augmented generation (RAG), orchestration, and multi-agent systems.
• Experience designing evaluation frameworks for agentic AI, including automated evals, benchmark datasets, LLM-as-a-Judge methodologies, human evaluation pipelines, and continuous quality measurement.
• Strong understanding of the tradeoffs between prompting, fine-tuning, retrieval, and agent orchestration, and when to apply each approach.
• Experience evaluating frontier foundation models across quality, latency, safety, cost, robustness, and production readiness.
• Proven ability to debug complex agent behaviors, identify failure modes, and improve reasoning, reliability, and overall system performance.
• Strong software engineering skills with experience building scalable distributed systems and production ML infrastructure.
• Demonstrated technical leadership through architecture design, mentorship, and influencing engineering direction across multiple teams.
• Experience with agent frameworks, AI observability platforms, model evaluation tooling, or regulated AI applications is a strong plus.