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This listing was closed on Jun 21, 2026.
Browse Open JobsLead Machine Learning Engineer
Summary by AIEngineer.careers
Lead Machine Learning Engineer is a Machine Learning Engineer role (full-time). with Wells Fargo. in CHARLOTTE, US. Compensation shown: $119K–$224K. Imported listing (source: tryjeremy.com). Apply on the employer's site (tryjeremy.com).
Imported listing (source: tryjeremy.com) · Apply on tryjeremy.com
CHARLOTTE · US
$119K–$224K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from tryjeremy.com
Wells Fargo's AI/ML Data Architecture team seeks a Lead Machine Learning Engineer to design and deploy scalable ML solutions on Google Cloud Platform. This role focuses on building data pipelines, enabling self-service analytics, and supporting predictive AI models in a hybrid work environment.
Responsibilities
- Design and implement scalable, secure data pipelines from internal systems to GCP services (BigQuery, BigTable, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).
- Enable self-service data consumption and governance through standardized patterns, templates, and sandbox capabilities.
- Design data architectures for training, validation, and monitoring of predictive ML and generative AI solutions.
- Define and implement standardized feature engineering and a common feature store with strong lineage and high availability.
- Optimize cost, performance, and reliability of GCP data workloads.
- Develop transformation libraries in Python/SQL/Beam for common patterns.
- Partner with data scientists to deliver new model use cases onto GCP.
Requirements
- 5+ years of Database Engineering experience or equivalent.
- 5+ years creating analytics/data science solutions in public cloud (GCP, AWS, Azure).
Wells Fargo
San Francisco · US · 205000+ employees
Wells Fargo is a diversified financial services company providing banking, investment, mortgage, and consumer and commercial finance products. It serves individuals, businesses, and institutions through various distribution channels including retail branches and digital platforms.
