Key Responsibilities

Develop and own the technical roadmap for Raspberry’s creative AI tools, spanning image generation, editing, and end-to-end design workflows.
Mentor and guide a small team of ML engineers and researchers, raising the bar on experimentation, code quality, system design, and impact.
Design and evolve the systems and model orchestration in order to improve quality, controllability, and realism in generated designs.
Conduct applied research and rapid experimentation on diffusion and other generative models, adaptation techniques, and routing strategies, and bring the most effective approaches into production.
Prototype and refine end-to-end design workflows for designers inside Raspberry and translate them into robust, production features.
Collaborate closely with product, design, and product engineering to shape creative workflows, quality standards, and the roadmap for AI-powered features.
Evaluate and integrate new techniques, frameworks, and open-source contributions to stay on the frontier of generative AI research.
Design and maintain pragmatic evaluation loops that combine automated metrics, LLM-assisted review, human feedback, and high-quality golden datasets.
Serve as a thought leader on AI innovation within Raspberry and across the broader industry.

Qualifications

Master’s or Ph.D. in Computer Science, Machine Learning, or a related field, or equivalent industry experience in applied ML roles.
7+ years of experience in applied machine learning or adjacent research-to-product roles with tangible shipped impact, including 3+ years leading significant technical projects and/or acting as a tech lead for other engineers.
Applied technical depth in several of the following areas: computer vision for generation/editing, diffusion or other generative model adaptation, model routing/orchestration, multimodal prompting, and tool use.
Strong practical experience with generative image and/or video models, including using and adapting modern diffusion or multimodal models in production systems.
Experience training and evaluating models on cloud GPU platforms (AWS, GCP, or Azure) and integrating external model APIs alongside open-source stacks.
Proven ability to take AI research to production, with experience building systems that balance innovation, performance, and maintainability.
Proficiency using and tuning multimodal LLMs to support prompting, routing, and evaluation workflows.
Familiarity with LLMs, CLIP-like architectures, and multimodal embeddings, and how they integrate into generative and evaluation pipelines.
Excellent communication and collaboration skills — able to work fluidly with designers, engineers, and business leaders alike.
Passion for creative AI and its ability to transform human expression and design workflows.

Nice to Have

Experience in computer vision for design, visual creativity or 3d/garment workflows.
Experience building tools or workflows for professional creatives (designers, artists, or adjacent domains).
Contributions to open-source AI projects, published research in top-tier venues (NeurIPS, CVPR, ICML, etc.), or sharing work publicly (talks, blog posts, tutorials, or research).
Comfort operating in an early-stage startup environment with fast iteration cycles.