Principal Machine Learning Engineer
Principal Machine Learning Engineer
Raspberry AI
Raspberry AI is a leading provider of industry-defining AI design software for fashion brands and retailers. Our software empowers brands to rapidly understand consumer demand and create unique designs within minutes. Leveraging cutting-edge AI analytics and generative AI capabilities, we help fashion brands revolutionize their design and merchandising processes.
We are a Series A startup, backed by top-tier venture capital firms such as Andreessen Horowitz, Khosla Ventures, MVP and Greycroft.
About the Role
This is a full-time remote role for a Principal Machine Learning Engineer at Raspberry AI. We are seeking a highly talented and motivated Machine Learning Engineer to join our growing ML team. In this role, you will focus on improving the quality and performance of our cutting-edge diffusion models, pushing the boundaries of generative AI in the fashion domain.
Additional responsibilities may be assigned as business needs evolve.
Responsibilities
Conduct applied research and experimentation on state-of-the-art diffusion model architectures and training techniques.
Implement and evaluate novel techniques for improving quality and controllability in generated designs.
Analyze and interpret experimental results, draw meaningful conclusions, and communicate findings effectively.
Collaborate closely with the team to translate prototypes into production-ready systems.
Stay abreast of the latest advancements in diffusion models, deep learning, and generative AI research.
Requirements
Master's or Ph.D. in Computer Science, Machine Learning, or a related field.
5+ years of industry experience.
Strong theoretical and practical understanding of deep learning, with a focus on generative models (e.g., GANs, VAEs, Diffusion Models).
Hands-on experience with deep learning frameworks such as PyTorch.
Experience with training and evaluating generative models on cloud GPU platforms (e.g., AWS, GCP, Azure).
Proficiency in using and tuning multimodal LLMs, including experience with both API-based and open-source model implementations.