Senior Deep Learning Engineer

About Browzwear

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 for complex geometric data. You will leverage your deep expertise to design, train, and validate sophisticated classifier and generative models, and you will have the unique opportunity to apply deep learning to solve complex computational challenges within our 3D technology stack.

What you will do

Design, implement, and train novel deep learning models for various tasks involving 3D geometric data, including classification and generation.
Develop and test advanced deep learning architectures for processing and analyzing 3D data formats, such as triangular meshes.
Explore and implement novel deep learning approaches to enhance and optimize our geometric algorithms and computational workflows.
Train and deploy models at scale using cloud computing platforms (e.g., AWS, GCP, Azure).1
Collaborate with fellow engineers and researchers to integrate deep learning models into our broader product and algorithmic pipelines.
Stay current with the latest advancements in deep learning, particularly in geometric deep learning and 3D computer vision.

What you bring

A Master's degree or Ph.D. in Computer Science, Engineering, Mathematics, or a related scientific field.
A strong foundation in core mathematical concepts, including linear algebra, calculus, and probability theory.
Proven experience designing and training deep learning models, with a specific focus on 3D geometric data (e.g., triangular meshes).
Strong programming skills in C++ and a good understanding of classical geometric algorithms.
Proficiency in a modern deep learning framework such as PyTorch or TensorFlow.
Experience training models in a cloud environment (e.g., AWS, GCP, Azure).

Nice to Have's

Specific experience with generative models (like GANs, VAEs, or Diffusion Models) for geometric data.
Prior experience applying machine learning to computational science or engineering problems.
Familiarity with mathematical topics such as differential geometry or topology.
Familiarity with specialized 3D deep learning libraries (e.g., Kaolin).
Experience with software development best practices, including version control (Git), CI/CD, and containerization (Docker).