Apply on Employer Site

Robinhood · 1 hour ago

Senior Machine Learning Engineer, Agentic

Menlo Park, CA

Full-time

Onsite

Senior Level

$187K/yr - $220K/yr

Robinhood is on a mission to democratize finance for all, and they are seeking a Senior Machine Learning Engineer to join their Agentic team. The role focuses on building AI agents and developing tools and workflows that support rapid prototyping and scalable experimentation in the financial domain.

CryptocurrencyFinTechStock ExchangesTrading Platform

Responsibilities

Design and create tools and workflows for agent development that support rapid prototyping—define agents, compose toolchains, and construct reasoning loops with minimal overhead

Build platform solutions to support scalable experimentation, synthetic dataset generation, and multi-agent evaluation across diverse tasks and domains

Develop feedback and optimization pipelines that incorporate both automated metrics and human-in-the-loop evaluation signals to fine-tune agent behavior

Implement and scale optimization techniques such as Direct Preference Optimization (DPO), Proximal Policy Optimization (PPO), and reward modeling to improve agent performance

Launch and support fine-tuned models in production environments with robust evaluation, rollback strategies, and performance monitoring

Collaborate closely with applied AI/ML teams to translate state-of-the-art research in agentic reasoning, planning, and tool use into reliable, production-ready systems

Qualification

Large Language ModelsAgentic workflowsML/AI systemsOptimization techniquesFeedback pipelinesInnovation mindsetLeadershipCommunication skillsCollaboration skills

Required

Strong technical expertise in software development, with understanding of agentic workflows—including reasoning loops, tool invocation, memory, and orchestration of autonomous AI agents

Hands-on experience using Large Language Models, including prompt engineering, fine-tuning, model distillation, and deploying optimized models (e.g. via DPO, PPO) into production environments

Proven ability to build and scale ML/AI systems, from experimentation to deployment—owning dataset generation, evaluation pipelines, A/B testing, and performance monitoring

Leadership and mentorship capabilities, with a track record of guiding complex technical projects and supporting the growth of teammates through code/design reviews and technical direction

Excellent communication and collaboration skills, with the ability to translate technical ideas into actionable plans and work effectively with cross-functional partners, including product and infrastructure teams

Innovation mindset and commitment to continuous learning and a bias toward action, staying at the forefront of ML/AI trends, agentic systems research, and best practices in tooling, safety, and evaluation