# Machine Learning Engineer - Multimodal Modeling at…

> Source: https://confidential.careers/job-detail/machine-learning-engineer-multimodal-modeling-educationpals-ai-san-francisco

# Machine Learning Engineer - Multimodal Modeling

EducationPals.ai 

** San Francisco, United States ** Full Time ** Engineering Jobs ** United States ** New

[Apply Now](https://confidential.careers/go/14125714) ** Save 

## Job Description

This posting is no longer verified. Similar roles and courses are still available.

Machine Learning Engineer - Multimodal Modeling San Francisco Employment Type Location Type Science & Engineering Compensation $250K – $295K • Offers Equity OverviewApplication Why Join Stand:At Stand, you’ll help build a new class of global property protection. We use advanced physics and AI to model catastrophic risk at the asset level, then automate underwriting and mitigation before loss occurs. Insurance is simply the current delivery mechanism.

The real product is a scalable risk engine, o

### Skills in this role

- AI fundamentals
- Machine learning
- Deep learning
- LLM evaluation

### Courses relevant to this posting

Relevance is based on skills and concepts — not a guarantee that you qualify.

- Structured Data Modeling for AI-Powered Analytics Covered skills: Machine learning LLM evaluation Skills to close: None listed

### Build the skills for this job

Coming-soon courses matched to this posting — outline, waitlist, and skill path.

Course DNA for this role

### Structured Data Modeling for AI-Powered Analytics

7 chapters · 30 lessons

- ### 1. Understanding Data Structure and Model Fit 4 lessonsLearn how data shape determines which model architectures will succeed or struggle.
- ### 2. Tabular Foundation Models and Specialized Architectures 4 lessonsExplore model families designed specifically for structured data patterns.
- ### 3. Task-Based Model Selection Frameworks 5 lessonsBuild decision trees for choosing models based on analytical task requirements.
- ### 4. Hybrid System Design Patterns 4 lessonsArchitect systems that route tasks to specialized models based on data and intent.
- ### 5. Evaluating Model Performance on Structured Data 4 lessonsMeasure accuracy, consistency, and reliability across tabular tasks.
- ### 6. Real-World Application Scenarios 5 lessonsApply model selection frameworks to customer analytics, finance, and operations use cases.
- ### 7. Implementation and Deployment Strategies 4 lessonsPlan rollout, monitoring, and iteration for multi-model analytics systems.

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Posted August 14, 2026