Lead Machine Learning Engineer

Wells Fargo

Lead Machine Learning Engineer designing and delivering advanced AI solutions at Wells Fargo. Collaborating with teams to optimize data and model pipelines for predictive insights and operational efficiency.

Posted 6/13/2026full-timeSan Francisco • California, North Carolina • 🇺🇸 United StatesSenior💰 $119,000 - $224,000 per yearWebsite

Tech Stack

Tools & technologies

AirflowAWSAzureBigQueryCloudGoGoogle Cloud PlatformOpen SourcePythonSQLVault

About the role

Key responsibilities & impact

Design and implement scalable, secure data pipelines from internal systems of record to Google Cloud Platform services (BigQuery, BigTable, Dataflow, Dataproc, Pub/Sub, Cloud Storage, Composer).
Leverage, extend and advise on capability roadmaps for reusable frameworks and tooling (ingestion, transformation, quality, orchestration) actively being developed by the larger organization.
Enable self ‑ service data consumption and governance by standardizing patterns, templates, and sandbox capabilities rather than one ‑ off pipelines.
Design data architectures for training, validation and monitoring of predictive machine learning as well as generative AI solutions.
Define and implement standardized feature engineering and a common feature store with strong lineage, dictionary and high availability for models.
Optimize cost, performance, and reliability of GCP data workloads (partitioning, clustering, storage classes, autoscaling strategies).
Develop transformation libraries in Python/SQL/Beam (e.g., common SCD patterns, data quality checks, masking/tokenization routines).
Provide orchestration capabilities via Cloud Composer or Cloud Workflows with reusable DAGs/templates and CI/CD integration.
Implement robust data modeling (dimensional, data vault, or canonical models) and semantic layer implementations with BigQuery or similar tools.
Enforce data quality, lineage, and observability using standardized metrics, validation rules, and monitoring dashboards.
Partner with data scientists and domain solution teams to develop and deliver new model use cases onto GCP capabilities.
Document patterns, runbooks, and best practices, and provide enablement through workshops and code examples.

Requirements

What you’ll need

5+ years of Database Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
5+ years of experience creating analytics or data science solutions in Public Cloud (GCP, AWS, Azure)
5+ years of hands on experience of Python and/or Go for building data pipelines, libraries, and automation tooling
5+ years with GCP or equivalent open source orchestration tools (Composer/Airflow/Dataflow/Beam) and CI/CD (Git, Liquibase, ) for data workloads
2+ years of hands-on experience building and implementing predictive AI models using machine learning algorithms (e.g., regression, classification, forecasting).

Benefits

Comp & perks

Health benefits
401(k) Plan
Paid time off
Disability benefits
Life insurance, critical illness insurance, and accident insurance
Parental leave
Critical caregiving leave
Discounts and savings
Commuter benefits
Tuition reimbursement
Scholarships for dependent children
Adoption reimbursement

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data pipelinesdata modelingfeature engineeringpredictive machine learningdata quality checksPythonSQLGoCloud ComposerCI/CD

Soft Skills

collaborationdocumentationworkshop facilitation