As a Senior Software Engineer on the AI Infrastructure team at Robinhood, you will play a crucial role in building and maintaining the machine learning platform that powers the company's AI products. This team focuses on creating a robust and centralized AI platform that enhances the capabilities of various teams across the organization. You will lead the architecture and delivery of systems that support model development, deployment, and observability. The role requires collaboration with ML practitioners and a commitment to high standards of reliability and scalability.

• 6+ years of software engineering experience, with depth in ML infrastructure, data engineering, or model operations.
• Demonstrated ability to own and deliver complex platform systems end-to-end, from architecture to production.
• Deep expertise in model serving, distributed systems, and production ML workflows at scale.
• Strong proficiency in Python, C++, or similar languages, and hands-on experience with ML frameworks such as TensorFlow or PyTorch.
• Solid knowledge of modern ML infrastructure tooling such as Ray, Kubeflow, SageMaker, and TensorFlow Serving.
• Hands-on experience with large-scale search systems, including embedding models and vector databases.
• Experience influencing technical direction across teams and mentoring engineers.
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field; advanced degree is a plus.

• Lead the architecture and end-to-end delivery of scalable systems for deploying, monitoring, and managing ML models in production.
• Own the technical direction for key platform areas, including model serving, the feature store, and ML observability infrastructure.
• Drive cross-functional partnerships with ML practitioners, data engineers, and applied AI teams to streamline workflows.
• Evolve and scale the feature store to support efficient, low-latency feature retrieval across real-time and batch use cases.
• Define and implement robust observability standards for model performance, data pipelines, and feature freshness.
• Manage and optimize cloud compute resources on AWS to support cost-effective, high-throughput training and inference.
• Contribute to technical strategy and roadmap discussions, and help mentor engineers on the team.