Senior Applied Scientist – Computer Vision & Machine Learning Location: Brazil (remote) Contract: PJ (long-term) We’re looking for a Senior Applied Scientist to join our Inbound team and build highly scalable machine learning solutions that power item attribute detection (e.G., color, material, style) and merchandise authenticity verification. If you’re passionate about solving real-world business problems using state-of-the-art Computer Vision and ML techniques — and enjoy taking ownership from research to production — we’d love to connect. What You’ll Do Develop and deploy Computer Vision and Machine Learning solutions to solve high-impact business challenges Design, build, and maintain clean, efficient, and scalable production-grade code Analyze large-scale datasets to extract insights and drive data-informed decisions Apply state-of-the-art ML methodologies (CNNs, Transformers, GANs, etc.) to build robust models Lead the technical direction of key components and systems Collaborate closely with Product, Engineering, and Tech Leads to deliver deployable solutions Mentor team members and help grow domain expertise Contribute to technical leadership through patents and publications Minimum Qualifications 5+ years of industry experience in Computer Vision and Applied Machine Learning Master’s or PhD in CS, ML, Statistics, or related field (or 8+ years of industry experience) 3+ years building, deploying, and managing ML/DL models in production at scale Deep understanding of ML/CV techniques: CNNs, Transformers, GANs, optimizers, regularization Strong experience with experiment design, A/B testing, training/serving pipelines, and feature engineering Proficiency in Python and scientific libraries (NumPy, Pandas) Hands-on experience with ML frameworks (PyTorch, TensorFlow, Keras, Scikit-learn) Strong data engineering skills with large-scale datasets Experience with experiment automation tools (Ray Tune, Weights & Biases, Kubeflow) Cloud experience (AWS, GCP, or Azure) Preferred Qualifications PhD in CS, ML, AI, Statistics, OR, or related field Experience applying ML to real-world retail problems Familiarity with MLOps pipelines and tools Strong ownership mindset with a focus on measurable business impact