Role overview
We are seeking a dedicated and ambitious individual to accelerate the development and expansion of products powered by Gen AI to democratize finance at an unprecedented pace. In this role, you'll play a key part in Robinhoodâs forward trajectory, collaborating closely with our adept Data Science and Engineering teams. The Gen AI team is devoted to bridging the transition of Gen AI & ML modeling work into production-grade applications. Robinhood operates at the intersection of data-driven insights and technological innovation.
The role is located in the office location(s) listed on this job description which will align with our in-office working environment. Please connect with your recruiter for more information regarding our in-office philosophy and expectations.
What you'll work on
In your role as a Machine Learning Engineer, you will focus on leveraging and optimizing Large Language Models (LLMs) along with the implementation of advanced AI technologies. Your key responsibilities will include:
- Development and Optimization of LLMs: Implement and fine-tune state-of-the-art Large Language Models for various applications, focusing on performance and accuracy.
- Evaluating Model Performance: Conduct rigorous evaluations of LLMs, assessing effectiveness, efficiency, and business alignment.
- Integration of Advanced AI Technologies: Implement Retrieval-Augmented Generation (RAG), function calling, and code interpreter technologies to enhance the capabilities of Large Language Models.
- Research and Development: Stay abreast of the latest advancements in machine learning, particularly in LLMs, LLM agents, and large-scale neural network training.
- Data and Model Parallel Training: Utilize data and model parallel training techniques for efficient handling of large-scale models.
- GPU Cluster Management for Training: Oversee extensive training jobs on GPU clusters, ensuring optimal resource utilization for complex tasks.
- Cross-Functional Collaboration and Leadership: Work with ML engineers, data scientists, and product teams, providing guidance and mentorship.
- Documentation and Reporting: Maintain detailed documentation of methodologies, models, and results, and communicate findings across the organization.