The trusted lender in real estate for bridge, rental and new construction loans. Kiavi helps property investors build and scale their portfolios with ease. Backed by Foundation Capital.
About the role
We are looking for a Senior Data Scientist to develop and apply modern machine learning and artificial intelligence across a range of products and business problems. The ideal candidate combines strong computer science foundations, rigorous experimental judgment, and the ability to turn advances in AI into reliable systems with measurable business impact. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Bachelor’s or advanced degree in Computer Science, Machine Learning, Electrical Engineering, Applied Mathematics, or a related technical field, or equivalent practical experience
- 5+ years of experience developing machine learning solutions, including experience taking models beyond initial experimentation
- Strong programming and software-engineering skills, particularly in Python
- Strong foundations in machine learning, statistics, algorithms, data structures, and experimental design
- Hands-on experience with deep-learning frameworks such as PyTorch or TensorFlow
- Experience training or fine-tuning transformer-based, vision, or multimodal models
More about this role
We are looking for a Senior Data Scientist to develop and apply modern machine learning and artificial intelligence across a range of products and business problems. The ideal candidate combines strong computer science foundations, rigorous experimental judgment, and the ability to turn advances in AI into reliable systems with measurable business impact.
The work spans document intelligence, multimodal systems that combine text, images, and structured data, and the adaptation of foundation models using proprietary datasets. These models support complex workflows in regulated financial services, where accuracy, explainability, data governance, reproducibility, and careful evaluation are essential.
You will work across the full machine learning lifecycle: framing problems, developing datasets, building and evaluating models, running large-scale experiments, and partnering with engineering teams to bring successful solutions into production. A central part of the role is turning successful approaches into reusable AI capabilities, patterns, and components that can support multiple teams and use cases rather than creating a separate one-off solution for every problem.
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