Pay
$166K–$240K is typical
The middle half of what similar US postings state. This one doesn’t list pay.
From 1,279 postings in the last year. Stated ranges, not salaries paid.
Applied Scientist AI/ML
Reads like a ML Engineer role: putting models into production.
Miami, FL
Live, first seen by us 19 days ago- Pay
$156,800–355,000/year · BaseLocation-specific pay — pay source
Compensation The base pay range for this position is $156,800-$355,000 annually, plus RSUs. Pay within this range varies by work location and may also depend on your qualifications, job-related knowledge, skills, and experience. We also offer a comprehensive package of benefits including unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees. #LI-RO
Read the full posting- Work setting
- Not stated
- Employment
Full-time — employment source
Employment type in the employer’s posting: SALARIED_FT
From the employer’s posting
Before you apply
- New graduates
- Entry level
A genuine interest in real estate — no prior experience required, but you'll engage deeply with housing data
What you’ll work on
Full postingWe’re looking for an Applied Scientist (ALL LEVELS) to push the boundaries of applied machine learning and AI at Opendoor.
Design and deploy architectural improvements to DNN-based home valuation models
Build interpretable ML models that explain pricing decisions to customers
Incorporate images, videos, and text into forecasting and valuation pipelines using LLMs and VLMs
Enhance human-in-the-loop pricing systems with Engineering and Ops
Improve feature engineering and model training pipelines for production systems
From the employer’s posting
We’re looking for an Applied Scientist (ALL LEVELS) to push the boundaries of applied machine learning and AI at Opendoor. While this role will have a significant impact on our valuation systems — ensuring we provide the most accurate and transparent pricing possible — the scope goes well beyond pricing. You’ll work across a range of challenging ML problems, from multi-modal modeling to operational optimization, helping us rethink how we use structured and unstructured data to make better decisions for our customers.
Design and deploy architectural improvements to our deep neural network (DNN)-based home valuation models
Build interpretable ML models that can help us explain pricing decisions to customers
Incorporate unstructured data — like images, videos, or text — into our forecasting and valuation pipelines using cutting-edge AI models (LLMs, VLMs, etc.)
Collaborate with Engineering and Ops to enhance our human-in-the-loop pricing systems
Improve the feature engineering and model training pipelines that power our production systems
What you’ll bring
All qualificationsCore experience
- Experience developing and deploying ML models end-to-end — from research and prototyping to implementation in production systems
- Strong communication and collaboration skills — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly
- Familiarity with Pyspark and distributed data processing
- Hands-on experience with deep learning architectures, including ConvNets, Transformers, or similar
- Experience working with large language models (LLMs) or vision-language models (VLMs)
Qualification wording
Experience developing and deploying ML models end-to-end — from research and prototyping to implementation in production systems
Strong communication and collaboration skills — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly
Familiarity with Pyspark and distributed data processing
Hands-on experience with deep learning architectures, including ConvNets, Transformers, or similar
Experience working with large language models (LLMs) or vision-language models (VLMs)
Education & alternatives
- 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
About Opendoor Technologies
At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one.
In the employer’s words · Read in context
Posting record
Report an error- Status in our records
- Live
- First seen by us
- Oct 17, 202619 days ago
- Recorded sightings
- 41
- Last seen by us
- Nov 5, 2026
- Employer says posted
- May 4, 2026
These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.
How this job compares
Time since we first saw it
Day 19
Similar jobs usually come down between day 6 and day 70.
From 4,515 similar postings that came down. Coming down is not always a hire.
Opendoor Technologies is hiring
7 open jobs
Open jobs by the week we first saw them, last 12 weeks.
Hiring recordSee how this role fits your experience
Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.
- Python
- PySpark
Where the posting names them
What You'll Need - 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 implementation in production systems
Nice to Have - Familiarity with Pyspark and distributed data processing - Background in search, recommendation systems, or personalization
Your next step
Complete your application on ats.rippling.com. The employer’s form will show what is required.
Applying through Rippling
- “The résumé will be parsed to fill in the application details”: check what it filled before you send.
Already applied? Track this application
Has Opendoor Technologies posted this job before?
Yes. We’ve followed 2 other Applied Scientist AI/ML jobs there since October 2026. All 2 are still up.
Demand for ML Engineer roles
New ML Engineer postings at the employers we follow are up 12% over the last 5 weeks compared with the 5 before.
Each bar is one week of new postings. The darkest bars are the last 5 weeks.
Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.
About the role
We file this job under ML Engineer, by the work the posting describes.
ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.
Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.
A data scientist often works out which model to build; an ML engineer makes it run reliably in production.
US government figures for computer and information research scientists
- Median yearly pay, May 2025
- $140,300
- Expected change, 2025 to 2035
- 21.8% more jobsagainst 3.5% for all jobs
The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.
Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.
Job description
About Opendoor
At Opendoor our mission is to tilt the world in favor of homeowners and those who aim to become one. Homeownership matters. It's how people build wealth, stability, and community. It's how families put down roots, how neighborhoods strengthen, how the future gets built. We're building the modern system of homeownership giving people the freedom to buy and sell on their own terms. We’ve built an end-to-end online experience that has already helped thousands of people and we’re just getting started.
About the Role
We’re looking for an Applied Scientist (ALL LEVELS) to push the boundaries of applied machine learning and AI at Opendoor. While this role will have a significant impact on our valuation systems — ensuring we provide the most accurate and transparent pricing possible — the scope goes well beyond pricing. You’ll work across a range of challenging ML problems, from multi-modal modeling to operational optimization, helping us rethink how we use structured and unstructured data to make better decisions for our customers.
What You'll Need
- 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 implementation in 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 — you’re comfortable working with cross-functional stakeholders and can communicate technical ideas clearly
Nice to Have
- Familiarity with Pyspark and distributed data processing
- Background in search, recommendation systems, or personalization
- Experience working with large language models (LLMs) or vision-language models (VLMs)
- A genuine interest in real estate — no prior experience required, but you'll engage deeply with housing data
What You'll Do
- Design and deploy architectural improvements to our deep neural network (DNN)-based home valuation models
- Build interpretable ML models that can help us explain pricing decisions to customers
- Incorporate unstructured data — like images, videos, or text — into our forecasting and valuation pipelines using cutting-edge AI models (LLMs, VLMs, etc.)
- Collaborate with Engineering and Ops to enhance our human-in-the-loop pricing systems
- Improve the feature engineering and model training pipelines that power our production systems
- Rethink our risk and optimization models using real-world data and domain insight
- We’re a small, nimble team — there’s ample opportunity to work across the entire research and modeling stack.
Compensation
The base pay range for this position is $156,800-$355,000 annually, plus RSUs. Pay within this range varies by work location and may also depend on your qualifications, job-related knowledge, skills, and experience. We also offer a comprehensive package of benefits including unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees.
#LI-RO
Source details
Source notes
Source excerptsSelected passages from the saved posting. Check the full description for conditions and exceptions.
- Pay
Compensation The base pay range for this position is $156,800-$355,000 annually, plus RSUs. Pay within this range varies by work location and may also depend on your qualifications, job-related knowledge, skills, and experience. We also offer a comprehensive package of benefits including unlimited PTO, medical/dental/vision insurance, life insurance, and 401(k) to eligible employees. #LI-RO
- Location & working pattern
Miami, FL
Working pattern and location restrictions need checking in the full posting.
- Work authorization
No clear work-authorization passage found. Eligibility is unconfirmed.