Software Engineer

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

Software Engineer at Robinhood focused on building and deploying machine learning models and AI-powered services for core financial platforms. Responsibilities include end-to-end ML pipeline implementation, model lifecycle management, production integration with low-latency inference, and monitoring. The role emphasizes applying software engineering best practices to ML codebases and ensuring compliance with security and privacy requirements.

What you'd actually do

  1. Build, test, and release product-facing features with stringent correctness and scalability requirements.
  2. Design, develop, and maintain machine learning models and AI-powered services that support product features and decision-making systems.
  3. Implement end-to-end ML pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment.
  4. Collaborate with product, data, and engineering teams to translate business problems into machine learning solutions.
  5. Own ML components throughout their full lifecycle, including experimentation, implementation, deployment, production monitoring, and iteration.

Skills

Required

  • Bachelor’s degree in Computer Science, Computer Engineering, Software Engineer or a related field (or foreign equivalent) and 3 years of experience in the job offered or related occupation.

Nice to have

  • machine learning models
  • AI-powered services
  • end-to-end ML pipelines
  • data ingestion
  • feature engineering
  • model training
  • evaluation
  • deployment
  • production monitoring
  • iteration
  • low-latency inference
  • reliable service operation
  • debugging
  • incident response
  • remediation efforts
  • testing
  • code reviews
  • documentation
  • version control
  • security
  • privacy
  • compliance requirements

What the JD emphasized

  • stringent correctness and scalability requirements
  • low-latency inference
  • security, privacy, and compliance requirements

Other signals

  • design, develop, and maintain machine learning models and AI-powered services
  • Implement end-to-end ML pipelines, including data ingestion, feature engineering, model training, evaluation, and deployment
  • Integrate ML models into production systems, ensuring low-latency inference and reliable service operation
  • Monitor model performance and data quality in production