Protecting Chime's members and platform from fraud, abuse, and financial risk
Requirements
- 5–7+ years of experience in AI/ML engineering or applied data science, with a track record of deploying production-grade ML systems
- Proven expertise in designing and scaling ML pipelines, including feature engineering, model training, deployment, and monitoring in production
- Strong knowledge of AI/ML methods - spanning both traditional approaches (e.g., tree-based models, logistic regression) and modern techniques (e.g., deep learning, graph-based ML, transformer-based architectures) - with the ability to apply the right method to fraud and risk detection problems
- Experience working with large language models (LLMs) (e.g., GPT, LLaMA, Claude) and related techniques such as fine-tuning, prompt engineering, or retrieval-augmented generation (RAG)
- Hands-on experience with unstructured data processing (e.g., text, images, documents). Familiarity with vector databases (e.g., FAISS, Pinecone) is a plus
- Proficiency in Python and SQL, with experience using distributed computing frameworks to build scalable AI/ML pipelines
Responsibilities
- Own the full ML lifecycle - design, build, deploy, and maintain production-grade models, ensuring scalability, performance, and reliability
- Work with diverse data types - analyze large structured and unstructured datasets to extract insights and identify opportunities for AI/ML applications
- Apply advanced AI/ML techniques - leverage approaches such as deep learning, generative AI, traditional ML, or anomaly detection to solve high-impact business problems
- Drive measurable outcomes - translate model insights into tangible business value by partnering with product managers, engineers, and business stakeholders
- Shape technical practices - define and advocate for best practices in model development, deployment, and monitoring to raise the quality bar across teams
- Contribute to the ML community - mentor peers, share knowledge, and foster collaboration across the organization to strengthen the AI/ML ecosystem
Other
- Strong collaboration skills and experience working closely with product, risk operations, and engineering partners to deliver AI/ML solutions aligned with business needs
- A track record of mentoring peers, setting technical standards, and influencing cross-functional stakeholders to raise the bar for AI/ML practices
- 5–7+ years of experience in AI/ML engineering or applied data science, with a track record of deploying production-grade ML systems
- Ability to work with diverse data types - analyze large structured and unstructured datasets to extract insights and identify opportunities for AI/ML applications
