• 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.

Qualifications:

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.

Company:

Robinhood is a stock brokerage that allows customers to buy and sell stocks, options, ETFs, and cryptocurrencies with zero commission. Founded in 2013, the company is headquartered in Menlo Park, California, USA, with a team of 1001-5000 employees. The company is currently Public Company.