Software Engineer II - Machine Learning

JPMorgan Chase JPMorgan Chase · Banking · Wilmington, DE +1 · Consumer & Community Banking

Software Engineer II on the Machine Learning Operations team at JPMorgan Chase, focusing on building and maintaining end-to-end ML solutions for customer-facing experiences. The role involves engineering scalable features and pipelines for relevance and ranking models, implementing ML Ops practices, and ensuring system reliability and observability in a production environment.

What you'd actually do

  1. Build and maintain software services and pipelines that support end-to-end machine learning solutions in production
  2. Engineer scalable features and feature pipelines used in relevance and ranking models, with a focus on performance and reliability
  3. Collaborate with machine learning, product, and engineering partners to translate customer and business needs into deliverable solutions
  4. Implement and improve machine learning operations capabilities, including model packaging, deployment, monitoring, and automation
  5. Develop and maintain automated tests and quality controls for data, features, and services to reduce risk and improve stability

Skills

Required

  • Formal training or certification on software engineering concepts and 2+ years applied experience
  • 2+ years of experience building software applications or services used in production
  • Proficiency in at least one programming language (for example, Java or Python)
  • Experience with data structures, algorithms, and writing efficient, reliable code
  • Experience with relational databases and writing queries (for example, SQL) and familiarity with NoSQL concepts
  • Experience with automated testing practices (unit and integration) and collaborative code review
  • Ability to troubleshoot production issues using logs/metrics and structured root-cause analysis
  • Understanding of secure coding practices and basic security concepts (authentication, authorization, input validation)
  • Strong communication skills and ability to work effectively in an agile, cross-functional environment

Nice to have

  • Experience working on relevance, ranking, search, recommendations, or personalization systems
  • Experience engineering features and feature pipelines for machine learning use cases
  • Familiarity with machine learning operations practices (deployment, monitoring, model/version management)
  • Experience with distributed data processing or streaming frameworks
  • Experience with cloud-native development and continuous integration/continuous delivery practices

What the JD emphasized

  • end-to-end machine learning solutions in production
  • scalable features and feature pipelines used in relevance and ranking models
  • machine learning operations capabilities

Other signals

  • machine learning operations
  • relevance and ranking problems
  • scalable features
  • production machine learning solutions