MS in Computer Science with a focus on Machine Learning
2+ years experience in AI training and deployment
Deep understanding of model architectures, training pipelines, and evaluation
Ability to explore and iterate on various machine learning methods
Strong algorithms and mathematics background
Proficiency in Python; experience with NumPy and PyTorch preferred
Responsibilities
Develop spatial generative AI models for renovation and home design
Adapt models based on user feedback for tailored design suggestions
Define datasets and collaborate with labeling agencies for data quality
Deploy AI models into production and engage with users on practical issues
Iterate on model performance through real-world testing
Benefits
Collaborative and innovative work environment
Opportunity to work with cutting-edge generative AI technologies
Hands-on experience with real-world applications
Engagement in full lifecycle model development
Potential for high-impact contributions to home design solutions
Full Job Description
We are looking for a Generative AI engineer to work on the full lifecycle of novel spatial generative AI model development, defining the correct frame of an inference problem, structuring image-based training data into usable formats, training and testing models, deploying performant systems into our production architecture stack.
Upcoming Challenges
Generate high quality renovation and home design layouts using state-of-the-art generative models.
Build a model that adapts to user feedback and suggests home design tailored to user preferences.
Define required datasets and work with labeling agencies to generate high quality data.
Deploy models to production environments and iterate with real users on real-world problems.
What we Require
MS in Computer Science with a focus or specialty in Machine Learning
2+ Years of experience training and deploying real-world AI . Deep understanding of model architectures, training pipelines, and evaluation methodologies.
Creativity and technical depth to explore, evaluate, and iterate on different machine learning approaches
Scrappiness and speed in getting models built and tested under real-world constraints.
Strong background in algorithms, data structures, and mathematics, with the ability to design performant solutions to spatial and generative problems.
Proficiency in Python, with a strong preference for experience using NumPy and PyTorch.
