Applied Scientist AI/ML

Opendoor

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Work type
On-site
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Posted
176 days ago
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Confirmed live today

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How this pay compares to similar roles

Similar $215k
$139k $277k
below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $177,250–$253,625.

Based on 240 similar postings.

Employer

About Opendoor

Opendoor is a digital real estate marketplace that buys and sells homes directly to consumers, simplifying the home selling and buying experience through instant offers and transparent pricing. Industry: Real Estate Technology & iBuying

Opendoor currently has 21 open roles on FindRole.

Listed pay typically runs $156,800–$335,000 across 6 roles with salary data.

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At a glance

TL;DR · Applied Scientist AI/ML

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The Applied Scientist AI/ML joins a small, nimble team to push the boundaries of applied machine learning and artificial intelligence. This role focuses on designing and deploying architectural improvements to deep neural network-based home valuation models while building interpretable models to explain pricing decisions. You will incorporate unstructured data, such as images, videos, and text, into forecasting pipelines using large language models and vision-language models. Key responsibilities include improving feature engineering, enhancing human-in-the-loop pricing systems, and rethinking risk and optimization models. You must possess strong software engineering skills in Python, experience with production codebases, and expertise in deep learning architectures like ConvNets and Transformers. The role requires an advanced degree in a quantitative field and a solid foundation in statistics and experimental design.

What does a Applied Scientist earn?

Median $208800 from 61 postings across 10 companies.

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Skills

Python Machine Learning Deep Learning LLMs VLMs Pyspark Transformers ConvNets DNN Distributed Data Processing Feature Engineering Statistics Experimental Design Production Systems Search Recommendation Systems

What you'll do

  • Design and deploy architectural improvements to deep neural network-based home valuation models.
  • Build interpretable ML models to explain pricing decisions to customers.
  • Incorporate unstructured data like images, videos, and text into forecasting and valuation pipelines.
  • Enhance human-in-the-loop pricing systems in collaboration with Engineering and Ops teams.
  • Improve feature engineering and model training pipelines for production systems.
  • Rethink risk and optimization models using real-world data and domain insights.

What we're looking for

  • Strong software engineering and coding skills in Python with experience contributing to production codebases.
  • Experience developing and deploying ML models end-to-end from research and prototyping to production systems.
  • Hands-on experience with deep learning architectures, including ConvNets, Transformers, or similar.
  • Advanced degree (MS or PhD) in computer science, statistics, mathematics, or a related quantitative field.
  • Solid foundation in statistics and experimental design.
  • Strong communication and collaboration skills to work with cross-functional stakeholders and communicate technical ideas.
  • Familiarity with Pyspark and distributed data processing (preferred).
  • Background in search, recommendation systems, or personalization (preferred).

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