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.
The base salary offered for this role and level of experience will begin at $172,000.00 and up to $238,000.00. Full-time employees are also eligible for a bonus, competitive equity package, and benefits. The actual base salary offered may be higher, depending on your location, skills, qualifications, and experience.
- 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
- 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