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Machine Learning Engineer - Multimodal Modeling

northern, ky • Posted 4 days ago
Hybrid Full Time Electrical & Energy Engineering

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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. 1. Understanding Data Structure and Model Fit

    4 lessons

    Learn how data shape determines which model architectures will succeed or struggle.

  2. 2. Tabular Foundation Models and Specialized Architectures

    4 lessons

    Explore model families designed specifically for structured data patterns.

  3. 3. Task-Based Model Selection Frameworks

    5 lessons

    Build decision trees for choosing models based on analytical task requirements.

  4. 4. Hybrid System Design Patterns

    4 lessons

    Architect systems that route tasks to specialized models based on data and intent.

  5. 5. Evaluating Model Performance on Structured Data

    4 lessons

    Measure accuracy, consistency, and reliability across tabular tasks.

  6. 6. Real-World Application Scenarios

    5 lessons

    Apply model selection frameworks to customer analytics, finance, and operations use cases.

  7. 7. Implementation and Deployment Strategies

    4 lessons

    Plan rollout, monitoring, and iteration for multi-model analytics systems.

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