Senior Apps Applied Scientist

The Applied Scientist will work on designing, developing, and implementing sophisticated machine learning and AI models to solve complex problems, particularly for creative applications like our image editing apps. The role involves building end-to- end ML pipelines, prototyping novel AI-powered features, developing AI tools, and collaborating closely with engineering, product, and marketing partners to bring intelligent solutions into production. The ideal candidate combines deep technical expertise in machine learning, statistical modeling, and AI framework development with strong problem-solving and interpersonal skills, ensuring effective collaboration and measurable impact in a fast-paced environment. PhD in Computer Science, Statistics, Mathematics, or a related quantitative field with 3+ years of relevant experience; or MS with 5+ years of experience in applied AI, machine learning, or statistical modeling. 3+ years of programming proficiency with Python for data science and AI (e.g., Pandas, Scikit-learn, NumPy). 3+ years of hands-on experience applying statistical modeling and machine learning algorithms for supervised and unsupervised learning (classification, regression, clustering, etc.). 3+ years of experience working with large-scale data and distributed systems (e.g., Hadoop, Spark). Working familiarity with causal inference models and techniques. Hands-on experience deploying AI/ML models into a production environment. Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG). Experience with rapid prototyping, reproduction, and validation of research ideas. Experience developing or contributing to AI frameworks, APIs, or internal tools used by other teams. Excellent presentation skills, distilling sophisticated analysis and concepts into concise business-focused takeaways. Strong product sense and a passion for user experience. Experience developing AI tools, frameworks, or APIs to support model deployment or LLM-based applications.

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