Senior Machine Learning Engineer

About this role

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 Senior 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. 3+ 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.

- Ability to effectively present complex technical information to both technical and non-technical audiences.

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