Deep expertise in building sequential and deep learning models, especially outside of traditional NLP applications and within financial or behavioral data domains
Strong experience across the end-to-end ML lifecycle, including training, experimentation, optimization, deployment, and monitoring
Experience working with large-scale transactional, financial, or behavioral datasets to develop predictive models
Hands‑on experience with AWS and modern ML infrastructure tools such as SageMaker, Kafka, Airflow, Redis, Snowflake, and Spark
Strong proficiency in Python, SQL, and distributed computing and model training frameworks such as PyTorch and PySpark for scalable ML development
A strong MLOps mindset with experience deploying and maintaining production‑grade ML systems
The ability to operate independently, collaborate cross‑functionally, and move quickly in ambiguous environments
What the job involves
We’re hiring for a Senior AI/ML Engineer, Growth & Marketing AI to help us build the next generation of AI‑powered growth and marketing capabilities at Chime
In this role, you’ll develop foundational transformer models that convert behavioral and financial data into highly personalized experiences, recommendations, and communications for millions of members
You’ll work closely with the Growth & Marketing team, as well as the Product and Engineering teams to deploy scalable AI systems that improve member engagement and drive company growth
This is a highly applied role where you’ll have the opportunity to work with rich datasets, solve challenging real‑world problems, and build cutting‑edge deep learning systems in production
Develop and deploy sequential deep learning models and traditional machine learning systems to power growth and marketing initiatives
Build predictive models using large‑scale financial, transactional, and behavioral datasets to improve personalization and member engagement
Partner cross‑functionally with Growth & Marketing, Product, and Engineering teams to drive strategic AI/ML initiatives
Design and improve infrastructure for training, serving, and monitoring large‑scale ML and deep learning systems in both batch and real‑time environments
Generate insights and recommendations that improve growth effectiveness and the overall member experience
Contribute to experimentation frameworks, optimization strategies, and scalable ML platform capabilities
Help identify technology gaps and opportunities where AI/ML solutions can create measurable business impact