Ai/ml Platform Engineer

at Opendoor · Consumer · Office - Washington-Seattle · Engineering

Opendoor is looking for an AI/ML Platform Engineer to build the scalable, production-grade platform and infrastructure for AI-powered pricing models, leveraging complex data sources like imagery and video. The role involves leading the architecture, design, and implementation of the AI/ML feature platform, designing large-scale data pipelines for AI-derived features, and ensuring seamless integration of ML capabilities into production. This is a technical leadership role focused on building the core AI/ML infrastructure.

What you'd actually do

Lead the architecture, design, and implementation of the AI/ML feature platform, focusing on scalability, reliability, and high performance for core business systems (e.g., pricing, transaction platforms).
Design and build large-scale data pipelines responsible for generating, indexing, and versioning AI-derived features (predictions, embeddings) from structured and unstructured data, with a specific emphasis on image and video data.
Ensure seamless integration of ML capabilities into production, setting technical standards for real-time AI consumption and agentic optimization within pricing and customer-facing products.
Partner closely with Data Science, Product, and Operations teams to translate business needs into technical requirements, ensuring the platform delivers tangible business outcomes like reduced spreads and increased transaction volume.
Serve as a technical mentor and leader, setting the technical direction and best practices for the new AI/ML platform group.

Skills

Required

AI/ML Platform Engineering
Scalability
Reliability
High Performance
Large-scale data pipelines
Feature Stores
Production AI/ML models
Distributed systems
Cloud infrastructure
Large-scale data processing
Vector search
Data versioning
Data infrastructure
API design
Machine learning consumption
Technical leadership (Staff/Senior Staff)
Computer vision pipelines
Image/video data for ML
Agentic optimization frameworks

Nice to have

MLOps
MLOps best practices
ML model deployment
ML model monitoring
ML model lifecycle management
ML model versioning
ML model testing
ML model debugging
ML model optimization
ML model security
ML model governance
ML model explainability
ML model interpretability
ML model fairness
ML model bias
ML model robustness
ML model resilience
ML model scalability
ML model performance
ML model availability
ML model reliability
ML model maintainability
ML model extensibility
ML model adaptability
ML model portability
ML model reusability
ML model composability
ML model modularity
ML model testability
ML model debuggability
ML model observability
ML model traceability
ML model auditability
ML model accountability
ML model transparency
ML model explainability
ML model interpretability
ML model fairness
ML model bias
ML model robustness
ML model resilience
ML model scalability
ML model performance
ML model availability
ML model reliability
ML model maintainability
ML model extensibility
ML model adaptability
ML model portability
ML model reusability
ML model composability
ML model modularity
ML model testability
ML model debuggability
ML model observability
ML model traceability
ML model auditability
ML model accountability
ML model transparency

What the JD emphasized

extensive experience as a technical leader (Staff/Senior Staff level) in large-scale, production software and data environments
proven track record of designing and implementing ML/Data Platforms or Feature Stores that directly support production AI/ML models
deep expertise in distributed systems, cloud infrastructure, and large-scale data processing (e.g., vector search, data versioning)
strong background in data infrastructure and API design, particularly serving data for machine learning consumption
experience in a domain where AI/ML is critical to the core business (e.g., pricing, optimization, recommendations)
prior experience with computer vision pipelines or handling large-scale image/video data for ML
familiarity with the technical challenges of managing compute and data versioning to optimize ML iteration cycles
experience with agentic optimization frameworks

Other signals

building scalable, production-grade platform and infrastructure necessary to support sophisticated ML models
designing and implementing ML/Data Platforms or Feature Stores that directly support production AI/ML models
large-scale data pipelines responsible for generating, indexing, and versioning AI-derived features (predictions, embeddings) from structured and unstructured data
seamless integration of ML capabilities into production
setting the technical direction and best practices for the new AI/ML platform group

Read full job description

About the Role

Opendoor is seeking an AI/ML Platform Engineer to join our Engineering team in our Seattle, WA office and will play a critical role in our strategic transition to AI-powered pricing models. This high-impact, business-critical position will focus on building the scalable, production-grade platform and infrastructure necessary to support sophisticated ML models, particularly leveraging complex data sources like imagery and video.

What You'll Do

Platform Leadership: Lead the architecture, design, and implementation of the AI/ML feature platform, focusing on scalability, reliability, and high performance for core business systems (e.g., pricing, transaction platforms).
Feature Engineering Infrastructure: Design and build large-scale data pipelines responsible for generating, indexing, and versioning AI-derived features (predictions, embeddings) from structured and unstructured data, with a specific emphasis on image and video data.
**AI Integration: **Ensure seamless integration of ML capabilities into production, setting technical standards for real-time AI consumption and agentic optimization within pricing and customer-facing products.
**Cross-Functional Collaboration: **Partner closely with Data Science, Product, and Operations teams to translate business needs into technical requirements, ensuring the platform delivers tangible business outcomes like reduced spreads and increased transaction volume.
**Mentorship & Direction: **Serve as a technical mentor and leader, setting the technical direction and best practices for the new AI/ML platform group.

What You'll Need

Extensive experience as a technical leader (Staff/Senior Staff level) in large-scale, production software and data environments.
Proven track record of designing and implementing ML/Data Platforms or Feature Stores that directly support production AI/ML models.
Deep expertise in distributed systems, cloud infrastructure, and large-scale data processing (e.g., vector search, data versioning).
Strong background in data infrastructure and API design, particularly serving data for machine learning consumption.
Experience in a domain where AI/ML is critical to the core business (e.g., pricing, optimization, recommendations).

Highly Sought-After Skills

Prior experience with computer vision pipelines or handling large-scale image/video data for ML.
Familiarity with the technical challenges of managing compute and data versioning to optimize ML iteration cycles.
Experience with agentic optimization frameworks.

Compensation

The base pay range for this position is $247,000.00 - $339,900.00 annually, plus RSUs and bonuses. 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.

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.