Software Engineer - ML Pricing

Opendoor Technologies Inc.•Miami, FL

30d•$143,200 - $179,000

About The Position

At Opendoor, pricing is at the core of our product - our models directly influence high-stakes decisions around real estate transactions across the country. We are looking for a mid-level Software Engineer to join our Pricing & ML team. This is a unique opportunity to work side-by-side with applied researchers and modelers to build and productionize the machine learning models that power our pricing engine. This role is perfect for an engineer who is excited to deepen their exposure to ML workflows. Our models are pragmatic and straightforward, and we care more about delivering value and reliability than optimizing hyperparameters or building complex research systems. You'll collaborate closely with researchers, bringing their ideas into production and contributing to the full ML lifecycle - experimentation, training, evaluation, deployment, monitoring, and iteration.

Requirements

Has 2-4 years of experience in software engineering, ideally with exposure to machine learning workflows.
Enjoys working closely with data scientists and researchers and has strong collaboration and communication skills.
Is comfortable navigating data pipelines, model training pipelines, and production environments.
Is fluent in writing maintainable, modular, and testable Python code.
Is motivated by impact and learning - not just building infrastructure for others.

Nice To Haves

Experience working on ML systems in business-critical environments (e.g., pricing, forecasting, logistics).
Familiarity with tools like MLflow, Airflow, Delta Lake, or Spark.
An interest in real estate or other messy, high-stakes domains with imperfect data.
Experience monitoring model performance in production (e.g., drift detection, quality alerts).

Responsibilities

Work closely with researchers and analysts to assist the development of pricing models, and convert prototypes into robust, production-grade code.
Own and operate model pipelines end-to-end - including training, validation, versioning, deployment, and monitoring.
Contribute to shared ML infrastructure and tooling as needed, while staying focused on solving business-critical problems.
Support day-to-day pricing model operations and address real-world ML lifecycle challenges like retraining, data drift, and model decay.
Navigate real estate-specific ML challenges (heterogeneous data, complex seasonality, sparse regions, high financial stakes).
Drive engineering best practices within the ML codebase - maintainable, testable, and scalable systems.