Senior Machine Learning Engineer, AI Infra

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. We partner deeply across Data, Platform, and Product Engineering to define how AI gets built and run at Robinhood — and we hold a high bar for reliability, scalability, and craft. If you’re energized by platform work that multiplies the output of an entire organization, this team is for you!

As a Senior Software Engineer on the AI Infrastructure team, you’ll be a technical anchor on our ML platform — owning the architecture and end-to-end delivery of foundational systems that power model development, deployment, and observability across the company. You’ll lead the design of complex platform capabilities including our feature store, model serving layer, and training infrastructure, while partnering closely with ML practitioners to ensure these systems accelerate their work rather than slow it down. You’ll bring senior-level judgment to ambiguous technical problems, contribute to the team’s technical strategy, and help mentor engineers earlier in their careers. Your work will directly shape how every AI product at Robinhood gets built, scaled, and maintained in production.

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. 

• →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 — from design through long-term reliability
• →Drive cross-functional partnerships with ML practitioners, data engineers, and applied AI teams to streamline workflows, reduce friction, and accelerate experimentation
• →Evolve and scale our 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 across the ML platform
• →Manage and optimize cloud compute resources (CPU/GPU) on AWS to support cost-effective, high-throughput training and inference at scale
• →Contribute to technical strategy and roadmap discussions, and help mentor engineers on the team through design reviews and hands-on guidance

• 6+ years of software engineering experience, with meaningful 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 (e.g., Ray, Kubeflow, SageMaker, TensorFlow Serving, Triton)
• Hands-on experience with large-scale search systems, including embedding models, vector databases, and distributed retrieval engines using platforms such as Qdrant, ChromaDB, or Elasticsearch with dense vector search capabilities
• Experience influencing technical direction across teams and mentoring engineers at varying levels
• Bachelor’s degree in Computer Science, Software Engineering, or a related technical field; advanced degree a plus