Senior Machine Learning Engineer, AI Platform & Agentic Apps

Robinhood·Menlo Park, CAsenior

En corto

→Construyes la plataforma base para agentes de IA que toman decisiones reales en finanzas.
→Diseñas evaluaciones de razonamiento y barreras de seguridad para garantizar confianza en agentes a escala.
→Plataforma de IA con impacto real: desde ingeniería hasta producción, con reglas y guardias que otros equipos adoptan.

Proficiency in English required for collaboration and documentation.

En ~1 minuto te damos: quién te entrevista, las preguntas probables con respuestas desde tu CV, y tu CV adaptado a esta vacante. Gratis, sin tarjeta.

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¿Qué piden?

✓Experiencia avanzada en aprendizaje automático y sistemas de IA
✓Habilidades sólidas en diseño de plataformas de agentes autónomos
✓Capacidad para construir evaluaciones de razonamiento (trajectory-level evals)
✓Experiencia con guardrails tecnológicos: permisos, aprobaciones, sandboxing
✓Experiencia en desarrollo end-to-end de aplicaciones de IA en producción
✓Compromiso con el diseño ético y seguro de sistemas de IA en entornos regulados

¿No cumplís todo? Es lo normal — tu dossier gratis te dice qué gaps tenés y cómo cubrirlos en la entrevista.

PythonML frameworks (e.g., PyTorch,Large Language Models (LLMs)Orchestration toolsTool integration platformsSimulation environmentsSynthetic task generationCI/CD pipelinesPermission & access control syRollback mechanisms

¿A quién escribirle en Robinhood?

Tu dossier gratis identifica a las personas que te entrevistarían — con su background, qué valoran y cómo escribirles para destacar antes de aplicar.

Join us in building the future of finance. 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. ABOUT THE TEAM + ROLE We are building an elite team, applying frontier technologies to the world's biggest financial problems. We're looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn't a place for complacency, it's where ambitious people do the best work of their careers. We're a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards. The AI Platform & Agentic Apps team builds the agent platform behind every AI agent at Robinhood. Today it gives a growing number of engineers and employees an AI teammate that ships code, queries data, and runs operational workflows on their behalf. We're building toward the same platform powering the agents millions of customers interact with directly, in real time. These agents don't just answer questions — they're designed to take real action across carefully curated meta harnesses. This is agentic AI at real scale, in a regulated financial environment, and it will change how Robinhood works! As a Staff Machine Learning Engineer on the AI Platform & Agentic Apps team, you will design and build the harness that every agent at Robinhood runs on. A critical part of the role is making those agents trustworthy at scale: trajectory-level evals that measure how an agent reasons and acts, and action guardrails — permission models, approval gates, and sandboxing — built as platform primitives that other teams adopt. You'll be a technical anchor on a growing, high-caliber team, collaborating with product, infrastructure, and fellow ML engineers to take ambitious ideas from zero to one and into production. You'll help define the team's technical direction, mentor engineers, and shape how Robinhood decides an agent is ready to ship. This role offers a rare combination of technical depth, platform-scale impact, and the satisfaction of building systems that genuinely don't exist anywhere else. This role is based in our Menlo Park, CA office, with in-person attendance expected at least 3 days per week. At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams. WHAT YOU'LL DO • Design and build the core of Robinhood's agent harness — orchestration, tool integrations, context and memory management — so one platform can safely power both high-trust internal agents and tightly scoped customer-facing ones. • Ship agentic applications end to end on that harness, from an ambiguous problem to a production agent that takes real action on behalf of employees or customers, and feed what you learn back into the platform. • Build trajectory-level evaluation systems that score how an agent got to an answer, not just the answer — tool-call correctness, planning and recovery, multi-step task completion — backed by simulation environments and synthetic task generation. • Architect action guardrails as platform primitives: least-privilege tool scoping, permission models, human-approval gates for high-risk or irreversible actions, step and budget limits, sandboxing, and rollback. • Make evals and guardrails products other teams adopt — SDKs, CI regression gates on prompt, model, and tool changes, continuou

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