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.