Senior Software Engineer, AI Infrastructure

Our mission is to democratize finance for all. An estimated $124 trillion of assets will be inherited by younger generations in the next two decades. The largest transfer of wealth in human history. If you’re ready to be at the epicenter of this historic cultural and financial shift, keep reading.

We are building an elite team, applying frontier technologies to the world’s biggest financial problems. We’re looking for bold thinkers. Sharp problem-solvers. Builders who are wired to make an impact. Robinhood isn’t a place for complacency, it’s where ambitious people do the best work of their careers. We’re a high-performing, fast-moving team with ethics at the center of everything we do. Expectations are high, and so are the rewards.

The AI Infrastructure team’s mission is to provide a robust, agile, and centralized AI platform—empowering teams across Robinhood to rapidly build, deploy, and iterate on high-quality Machine Learning (ML) and Generative AI (GenAI) applications at scale.

As a Senior Software Engineer, you’ll help evolve our ML platform and services to support scalable, reliable, and secure model development and deployment. You’ll work closely with Data Scientists and Applied ML Engineers to build foundational systems that accelerate experimentation, improve model observability, and drive business impact through AI.

At Robinhood, we believe in the power of in-person work to accelerate progress, spark innovation, and strengthen community. Our office experience is intentional, energizing, and designed to fully support high-performing teams.

• →Design, build, and maintain scalable systems for deploying, monitoring, and managing Machine Learning 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, enabling 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) to support cost-effective training and inference across AWS.

• 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 modern 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 embedding models, vector databases, and distributed retrieval engines, using platforms like Qdrant, ChromaDB, Elasticsearch etc. with dense vector search capabilities.
• A collaborative mindset with an interest in building reliable platforms that support company-wide impact.