Software Engineer, Agentic AI

Robinhood Robinhood · Fintech · [ProspectLand] · [Prospect]

Software Engineer, Agentic AI at Robinhood, focusing on building and implementing distributed systems for the full ML lifecycle, including data ingestion, model training, deployment, and monitoring. The role involves developing backend services, optimizing compute and data systems, and leveraging cloud infrastructure to deliver AI-driven capabilities in finance.

What you'd actually do

  1. 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.
  2. Develop and maintain robust backend services and APls (RESTful and gRPC) in programming languages such as Python or Go.
  3. Optimize compute, storage, and data retrieval systems using relational, NoSQL, and vector databases to enable low-latency model inference and intelligent data access.
  4. Collaborate with cross-functional teams to deliver Al-driven capabilities in domains such as market data, trading, accounting, authentication, and security.
  5. 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.

Skills

Required

  • Python
  • Go
  • distributed systems
  • cloud-based systems
  • machine learning lifecycle
  • backend services
  • APIs (RESTful and gRPC)
  • relational databases
  • NoSQL databases
  • vector databases
  • model inference
  • intelligent data access
  • AWS or GCP
  • Docker
  • Kubernetes
  • CI/CD workflows
  • model serving
  • observability systems
  • algorithms
  • data structures
  • compute efficiency
  • data efficiency
  • ML infrastructure
  • prototyping
  • evaluating new AI tools
  • frameworks
  • system designs
  • scalability
  • performance optimization
  • cost optimization
  • code reviews
  • automated testing
  • version control
  • agile environment

Nice to have

  • experience with open-source projects

What the JD emphasized

  • agentic AI
  • frontier technologies
  • low-latency model inference
  • Al-driven capabilities
  • large-scale compute environments for Al workloads

Other signals

  • building agentic AI systems
  • applying frontier technologies to financial problems
  • delivering AI-driven capabilities