Senior Ai/ml Engineer

Chime Chime · Fintech · San Francisco, CA · Data Science & Machine Learning

Senior AI/ML Engineer focused on Growth & Marketing AI at Chime. The role involves developing foundational transformer models for personalized experiences, recommendations, and communications using behavioral and financial data. Responsibilities include building predictive models, designing training/serving infrastructure, and contributing to ML platform capabilities. Requires expertise in sequential deep learning, end-to-end ML lifecycle, large-scale datasets, AWS, Python, SQL, PyTorch, and MLOps.

What you'd actually do

  1. Develop and deploy sequential deep learning models and traditional machine learning systems to power growth and marketing initiatives
  2. Build predictive models using large-scale financial, transactional, and behavioral datasets to improve personalization and member engagement
  3. Partner cross-functionally with Growth & Marketing, Product, and Engineering teams to drive strategic AI/ML initiatives
  4. Design and improve infrastructure for training, serving, and monitoring large-scale ML and deep learning systems in both batch and real-time environments
  5. Generate insights and recommendations that improve growth effectiveness and the overall member experience

Skills

Required

  • building sequential and deep learning models
  • financial or behavioral data domains
  • end-to-end ML lifecycle
  • training
  • experimentation
  • optimization
  • deployment
  • monitoring
  • large-scale transactional, financial, or behavioral datasets
  • predictive models
  • AWS
  • SageMaker
  • Kafka
  • Airflow
  • Redis
  • Snowflake
  • Spark
  • Python
  • SQL
  • distributed computing
  • PyTorch
  • PySpark
  • MLOps mindset
  • deploying and maintaining production-grade ML systems
  • operate independently
  • collaborate cross-functionally
  • move quickly in ambiguous environments

What the JD emphasized

  • foundational transformer models
  • highly personalized experiences
  • scalable AI systems
  • end-to-end ML lifecycle
  • production-grade ML systems

Other signals

  • develop foundational transformer models
  • deploy scalable AI systems
  • build cutting-edge deep learning systems in production
  • end-to-end ML lifecycle
  • MLOps mindset