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# Software Engineer, Agentic AI

- **Company:** Robinhood
- **Location:** Bellevue, WA, United States (Remote available)
- **Salary:** $117,749.0 - $195,000.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, User Authentication, Automation of Tests, Cloud Computing, Code Review, Databases, Data Retrieval, Fault Tolerance, Data Intelligence, Python (Programming Language), Machine Learning, NoSQL, Software Construction, Data Ingestion, Apache Spark, Reliability of Systems, Backend, Kubernetes, Information Technology, Low Latency, Performance Monitor, Machine Learning Operations, Virtual Agents, Restful APIs, Software Version Control, Docker, Programming Languages
- **Published:** June 4, 2026
- **Apply:** https://seeker-sp.worksourcewa.com/jobview/GetJob.aspx?JobID=293460508

## About the Role

Minimum Requirements for Position: Bachelor's degree or foreign academic equivalent in Computer Science or a related field. Education and/or experience must include:

 * Kubernetes
 * Spark
 * Machine Learning
 * Python
 * Databases
 * AWS

## Description

Robinhood Markets, Inc. has an opening in Bellevue, WA for Software Engineer, Agentic AI (9902208): Architect and implement distributed and cloud-based systems that support the full machine learning lifecycle - from data ingestion and preprocessing to model training, deployment, and monitoring. Develop and maintain robust backend services and APls (RESTful and gRPC) in programming languages such as Python or Go. Optimize compute, storage, and data retrieval systems using relational, NoSQL, and vector databases to enable low-latency model inference and intelligent data access. Collaborate with cross-functional teams to deliver Al-driven capabilities in domains such as market data, trading, accounting, authentication, and security. Leverage cloud infrastructure (e.g., AWS or GCP) and container orchestration technologies (e.g., Docker, Kubernetes) to manage large-scale compute environments for Al workloads. Design and maintain automated Cl/CD workflows to ensure efficient and reliable code
delivery. Implement model serving and observability systems to monitor performance, ensure fault tolerance, and improve system reliability. Apply algorithms and data structures to enhance compute and data efficiency across the ML infrastructure stack. Prototype and evaluate new Al tools, frameworks, and system designs to validate concepts and optimize performance prior to large scale rollout. Drive continuous improvement by identifying opportunities for scalability, performance, and cost optimization. Contribute to open-source projects (where possible) and uphold software engineering best practices including code reviews, automated testing, and version control in an agile environment. Telecommuting permitted.


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