Senior Software 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 is building the foundation that powers machine learning and AI across Robinhood. We own the infrastructure supporting the entire ML/AI lifecycle, from feature engineering and training pipelines to model serving, inference, and observability. We also build and operate Robinhood's LLM gateway, enabling engineering teams across the company to leverage frontier and fine-tuned in-house AI models efficiently and at scale.

As a Senior Software Engineer, AI Infrastructure, you'll join a small, growing team tackling some of the most interesting infrastructure challenges at the intersection of distributed systems and AI. You'll work across traditional ML infrastructure and emerging LLM technologies, helping improve GPU efficiency, scale model serving, and build intelligent routing systems. This is an opportunity to get in early, take meaningful ownership, and help shape the infrastructure that will support Robinhood's next generation of AI capabilities.

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 scale the infrastructure powering machine learning and AI across Robinhood, including feature stores, training pipelines, model serving, inference, and observability.
• →Build and optimize distributed systems that support large-scale ML/AI workloads, with a focus on performance, reliability, scalability, and latency.
• →Improve GPU utilization and efficiency by developing scheduling solutions for training and inference workloads.
• →Help evolve Robinhood's LLM gateway, including intelligent model routing, query and prompt understanding, and context optimization.
• →Support infrastructure for serving open-weight and fine-tuned models, enabling reliable access to AI capabilities across the company.
• →Partner with engineering teams across Robinhood to understand their infrastructure needs and build scalable, reusable solutions.
• →Own the operational health of critical infrastructure, including monitoring, troubleshooting, performance optimization, and participation in an on-call rotation.

• Strong software engineering experience with a focus on infrastructure, distributed systems, or large-scale backend platforms.
• Hands-on experience designing, building, and operating distributed systems in production, with a strong understanding of scalability, reliability, and system performance.
• Experience solving complex infrastructure challenges, including latency optimization, resource utilization, and production system reliability.
• Strong software development and system design skills, with the ability to take ownership of complex technical problems from design through production.
• Experience working with infrastructure technologies and tools in production environments, with an understanding of how to build and operate reliable systems at scale.
• Ability to work independently, navigate ambiguity, and contribute to foundational engineering projects within a small, growing team.
• Interest in machine learning and AI infrastructure, with a willingness to learn and work across both traditional infrastructure and emerging AI technologies.
• Bonus: Experience with ML infrastructure, model training or serving, GPU workload scheduling, LLM gateways, or open-source infrastructure technologies.