Machine Learning Engineer, GenAi

About the team + role

We are seeking a dedicated and ambitious individual to accelerate the development and expansion of products powered by Gen AI to democratize finance forl all at an unprecedented pace.

In this role, you'll play a key part in Robinhood’s 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.

What you’ll do

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:

1. Development and Optimization of LLMs: Implement and fine-tune state-of-the-art Large Language Models for various applications, focusing on performance and accuracy.
2. Evaluating Model Performance: Conduct rigorous evaluations of LLMs, assessing effectiveness, efficiency, and business alignment.
3. Integration of Advanced AI Technologies: Implement Retrieval-Augmented Generation (RAG), function calling, and code interpreter technologies to enhance the capabilities of Large Language Models.
4. Research and Development: Stay abreast of the latest advancements in machine learning, particularly in LLMs, LLM agents, and large-scale neural network training.
5. Data and Model Parallel Training: Utilize data and model parallel training techniques for efficient handling of large-scale models.
6. GPU Cluster Management for Training: Oversee extensive training jobs on GPU clusters, ensuring optimal resource utilization for complex tasks.
7. Cross-Functional Collaboration and Leadership: Work with ML engineers, data scientists, and product teams, providing guidance and mentorship.
8. Documentation and Reporting: Maintain detailed documentation of methodologies, models, and results, and communicate findings across the organization.

What you bring

• Advanced Degree: Master's or PhD in Computer Science, AI, Linguistics, or related fields, with a focus on machine learning and natural language processing.
• Experience with LLMs and PyTorch: Extensive experience with large language models and proficiency in PyTorch.
• Expertise in Parallel Training and GPU Cluster Management: Strong background in parallel training methods and managing large-scale training jobs on GPU clusters.
• Analytical and Problem-Solving Skills: Ability to address complex challenges in model training and optimization.
• Leadership and Mentorship Capabilities: Proven leadership in guiding projects and mentoring team members.
• Communication and Collaboration Skills: Effective communication skills for conveying technical concepts and collaborating with cross-functional teams.
• Innovation and Continuous Learning: Passion for staying updated with the latest trends in AI and machine learning.

Our team is here to enable an inclusive and welcoming interview experience for all candidates. If you need additional assistance throughout the interview process related to a physical or mental condition, or if there is something our team can do to enable a more accessible experience at any time, please notify our team by completing this Applicant Accommodation Form.

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Last updated on Aug 22, 2024