Applied AI Scientist

The Applied AI Scientist will work on designing, developing, and implementing sophisticated machine learning and AI models to solve complex problems, particularly for creative applications like our video 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 deep learning algorithms (CNN, RNN, Transformers) and frameworks (TensorFlow, PyTorch). Working familiarity with causal inference models and techniques. Hands-on experience deploying AI/ML models into a production environment. 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 analytical, communication, and collaboration skills across multi-functional teams. Experience with LLM fine-tuning, prompt engineering, or retrieval-augmented generation (RAG). Familiarity with generative AI techniques (e.g., diffusion models). Knowledge of computer vision (CV), multimodal models, and video generation/understanding, and their application in video or image analysis. Experience building scalable AI/ML systems using cloud platforms (AWS, GCP, Azure) and MLOps tools (e.g., SageMaker, Vertex AI, MLflow). Experience applying statistical and ML techniques to analyze user behavior, content usage, conduct feature analytics, and derive product insights. Hands-on experience with production model monitoring, including tracking data/model drift and running diagnostics. Proven ability to translate complex research ideas into scalable, production-level AI solutions. Strong software engineering practices, including version control, testing, and code review. Familiarity with cross-domain applications of AI/ML (e.g., marketing analytics, personalization, recommendation systems).

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