Machine Learning Engineer

Direct message the job poster from OpenHouse.ai

OpenHouse.ai is one of the fastest-growing data science companies disrupting the $300B+ new home construction industry by empowering technology-minded volume home builders to make effective, data-driven decisions. We're a team of highly skilled data scientists, engineers, and experienced thought leaders in the housing industry. Our ethos is deeply rooted in the democratization of technology—traditionally available only to the wealthiest, most digitized industries.

Our Quantitative AI platform has been deployed in more than 30 geo-markets across the U.S. and Canada, powering hundreds of new construction communities with physics-informed and market-proven models. We’re expanding our Quantitative Research team to accelerate our growth and solidify our position as the leading intelligence engine for the new home construction industry.

About the Role:

We are looking for a highly skilled Machine Learning Engineer with a solid foundation in applied mathematics or machine learning, a strong desire to learn, and an insatiable curiosity.

Reporting directly to the Head of R&D and being mentored by our CEO, you are expected to learn fast, stay on top of frontier AI research, and directly contribute to pre- and post-commercialization products at OpenHouse.ai. You’ll have the opportunity to build and refine advanced ML models while gaining deep exposure to the housing industry, an economic powerhouse, where your work could improve the lives of millions of families over the coming decade.

You will work in a productive and collaborative environment, using real-world data to design and stress-test models across various market conditions. Along with our team of strong data engineers and scientists, you will challenge conventional wisdom with grounded insights while creating systems that will impact billions of dollars of revenue a year for new home builders.

If you're excited about the idea of disrupting an antiquated industry by solving incredibly challenging problems under seemingly impossible constraints, we want to hear from you.

Your Typical Responsibilities:

• Co-design and engineer scalable, cost-effective Auto-ML infrastructure for data cleaning, feature engineering, training and inference for commercially successful models.
• Investigate new data sources for potential features and detect and manage outliers and anomalies in existing data sources.
• Ingest and clean real-world data and thoughtfully engineer synthetic data according to the chosen objective functions.
• Develop, run and field test prototypes in conjunction with our key customers during beta phases.
• Create and finetune classical machine learning and stochastic simulation models for various predictive and optimization problems.
• Collaborate with talented, driven colleagues on research, model design and code.
• Internally present findings for knowledge sharing, feedback and validation, and work with internal stakeholders to understand business problems.
• Research new and existing technology and stay curious about anything and everything to do with data science and machine learning to both implement and share with your peers.

About you:

• A highly skilled software engineer with a solid background in physics, applied mathematics, operations research or machine learning algorithms.
• Proficient in Python; experience with Pytorch and/or TensorFlow is an asset.
• Experience using Jupyter notebooks or similar research-phase tools to quickly explore data/models is an asset.
• Good understanding of the entire machine learning pipeline, from data ingestion to model deployment.
• Experience with, or ability to understand, data warehouses, data lakes and legacy ERP databases.
• Strong fundamental understanding of SQL and SQL analytic functions.
• Have a basic understanding of statistical distributions, analyze histograms, and generally intuitively understand data sets when exploring potential features.
• A strong desire to learn, or experience with creating quantitative models using a variety of techniques such as linear models, sparse matrix multiplication, dense neural networks, CNN, Transformer and other machine-learning architectures.
• A strong desire to learn or experience with LLMs and Autonomous Agents is an asset.
• Solid foundation in collaborative coding and use of tools like Git.
• No minimum work experience is required.

Compensation:

$80,000.00-$135,000.00 per year