Browzwear is a leading provider of 3D fashion design and simulation software, driving digital transformation in the fashion industry. We have pioneered the use of 3D design for apparel and is widely adopted by major fashion brands and manufacturers.

We are seeking a highly skilled and motivated Senior Deep Learning Engineer to join our innovative team. In this pivotal role, you will be at the forefront of developing cutting-edge solutions utilizing geometric, image and user data. You will leverage your deep expertise to design, train, validate and deploy sophisticated deep learning and generative models into our suite of products for digital creators.

WHAT YOU WILL DO

• Architect, train and deploy novel deep learning models and workflows for tasks including 3D data manipulation, texture generation, image generation and asset classification.
• Design and experiment with state-of-the-art architectures for processing geometric data, such as Graph Neural Networks (GNNs) for triangular meshes and diffusion models for image generation.
• Take ownership of the full MLOps lifecycle, from training and optimizing models at scale on cloud platforms (AWS, GCP, Azure) to deploying them as robust, production-ready services.
• Stay at the forefront of academic research in deep learning, generative AI, and 3D computer vision, and champion the adoption of new techniques to solve key product challenges. • Work closely with product managers, engineers, and researchers to integrate your models into our broader product pipeline. Mentor junior engineers and contribute to our team's technical culture and best practices.

• A Master's degree or Ph.D. in Computer Science, Engineering, Mathematics, or a related scientific field.
• 5+ years of post-academic, hands-on experience building, training and shipping machine learning models into production.
• A strong foundation in core mathematical concepts, including linear algebra, calculus, and probability theory.
• Expert-level proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
• Demonstrable experience designing and training deep learning models like, Convolutional neural networks (CNNs), Graph Neural Networks (GNNs), Generative adversarial networks (GANs), Diffusion models or similar.
• Hands-on experience with training and deploying models in a major cloud environment (e.g., AWS SageMaker, GCP Vertex AI, Azure ML).

Nice to Have's

• Deep, practical experience with generative models (e.g., GANs, VAEs, Diffusion Models).
• Prior experience in 3D computer graphics, computational geometry, or applying machine learning to engineering problems.
• A track record of publications in top-tier AI/ML conferences (e.g., NeurIPS, ICML, CVPR, SIGGRAPH).
• Experience with model optimization for performance (e.g., quantization, pruning, TensorRT).
• Experience leading complex technical projects from conception to completion.