Tech Stack

Description

You will design and build scalable systems for deploying and monitoring machine learning models in production, partner with ML practitioners to streamline workflows, and contribute to the development of a feature store and observability infrastructure. This role focuses on empowering teams across Robinhood to rapidly build and iterate on ML and generative AI applications at scale.

Requirements

• 4+ years of software engineering experience, ideally within ML infrastructure, data engineering, or model operations
• Hands-on experience with model serving, distributed systems, or production ML workflows
• Familiarity with ML infrastructure tools (e.g., Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton)
• Strong proficiency in Python, C++, or similar languages, and experience with ML frameworks like TensorFlow or PyTorch
• Hands-on experience with large-scale search systems including embeddings, vector databases, and distributed retrieval (e.g., Qdrant, ChromaDB, Elasticsearch)

Responsibilities

• Design, build, and maintain scalable systems for deploying, monitoring, and managing ML models in production
• Partner with ML practitioners to streamline workflows, integrate internal ML libraries, and optimize model performance
• Contribute to the development and scaling of our feature store for efficient feature retrieval across real-time and batch use cases
• Implement robust observability for model performance, data pipelines, and feature freshness
• Manage and optimize cloud compute resources (CPU/GPU) for cost-effective training and inference on AWS