Senior Applied Scientist, Rich Media Experiences

• Frame and solve complex perception problems using scientific and engineering best practices
• Collaborate with product, engineering, and design teams to translate user needs into research questions and solutions
• Design, implement, and iterate on machine learning and computer vision models for structured understanding of spaces
• Develop robust evaluation pipelines and experiments to measure and improve model performance
• Integrate models into production systems, ensuring reliability and scalability
• Monitor and improve deployed models based on real-world data and user feedback
• Mentor and support team members in modeling, evaluation, and research practices
• Communicate findings and technical decisions clearly to both technical and non-technical partners

• 5+ years of experience as an applied or research scientist working on machine learning or computer vision with real-world data
• Proficiency in Python and at least one deep learning framework (e.g., PyTorch, TensorFlow, or JAX), with a track record of building and deploying models
• Experience shipping production ML systems, including data pipelines, deployment, monitoring, and iteration
• Strong understanding of probability, statistics, and experimental design, with the ability to apply these to practical evaluation strategies
• Demonstrated ability to work with noisy, imperfect datasets and design robust solutions for challenging edge cases
• Experience with geometry-heavy or spatial understanding problems, or multi-modal/sensor-fusion challenges, is a plus
• Proven ability to communicate complex technical ideas to both technical and non-technical audiences, and to collaborate effectively in cross-functional teams
• Prior success in ambiguous, evolving problem spaces or zero-to-one environments is valued
• Contributions to the broader ML or computer vision community (e.g., publications, patents, open-source) are a plus

• Experience with geometry-heavy or spatial understanding problems, or multi-modal/sensor-fusion challenges, is a plus
• Contributions to the broader ML or computer vision community (e.g., publications, patents, open-source) are a plus

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