# Machine Learning Engineer - Embedded Insights

**Company:** [Plaid](https://hotfix.jobs/companies/plaid)
**Location:** San Francisco, CA, New York, NY, Seattle, WA
**Role:** ML Engineering
**Salary:** $212k – $272k/yr
**Experience:** 5+ years
**Skills:** Python, SQL, Machine Learning, Data Visualization, Model Deployment, Statistical Analysis, Data Analysis, Model Monitoring, Feature Engineering, MLOps
**Posted:** 2026-06-18

> Drive ML initiatives from concept to production on the Embedded Insights team. Identify opportunities, build and deploy models using Plaid's financial datasets, and partner with product teams to deliver scalable customer-facing intelligence products.

## Job Description

## Responsibilities
- Drive machine learning initiatives from concept to production across the full model development lifecycle
- Leverage Plaid’s unique datasets to identify high-impact opportunities for machine learning
- Develop proofs of concept to validate new approaches and build MVP solutions that demonstrate customer value
- Partner closely with product managers, engineers, and cross-functional stakeholders to embed within product teams
- Translate successful prototypes into scalable, customer-facing products
- Optimize models for new use cases, improve system scalability, and incorporate customer feedback
- Maintain and enhance existing machine learning systems through feature development, retraining strategies, and robust monitoring frameworks (metrics, alerts, dashboards)

## Requirements
- 5+ years of experience in machine learning, including deploying machine learning models into real-world, customer-facing systems
- High agency and creativity; experience identifying, defining, and proposing high-impact machine learning opportunities
- Ability to analyze large and complex financial datasets to derive insights
- Advanced degree or equivalent work experience in Statistics, Economics, Mathematics, Data Science, or a related field
- Proficiency in SQL, Python, and data visualization/analysis tools
- Ability to clearly communicate complex technical systems and decision making

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