Machine Learning Engineer (Generative AI)

Overview

Generative AI is poised to transform fashion, but off-the-shelf models fail to deliver the product accuracy and brand consistency that enterprises require. We're a well-funded, Swiss-based startup looking for a senior engineer to tackle the core technical challenges of generative AI in a high-stakes visual domain.

Responsibilities

• Develop and implement novel fine-tuning and model adaptation techniques to solve for character consistency and garment accuracy on top of foundational diffusion models.
• Design and build robust evaluation pipelines from the ground up to quantitatively and qualitatively measure photorealism, texture fidelity, and product accuracy.
• Own the R&D cycle for new generative features: rapidly prototype, test, and integrate the latest research and models from the community into our production environment.
• Create workflows to support creativity and completely eliminate boring tasks for fashion powerhouses employees.
• Collaborate directly with the founding team to architect and scale our AI inference stack for performance and reliability.

Qualifications

• MSc or PhD in Computer Science or equivalent provable experience
• 2+ years of industry experience in Machine Learning, OR a relevant PhD with a strong portfolio of applied projects.
• Deep, hands-on experience with Generative AI, specifically Diffusion Models and their application.
• Expert proficiency in Python and PyTorch.
• Proven experience taking ML models from research/prototype to production.
• Solid software engineering fundamentals (Git, Docker, CI/CD).

Benefits

1. Autonomy & Impact: You won't be a ticket-taker. You'll have the agency to own the R&D lifecycle for core features, from hypothesis to production, and see your work ship.
2. Competitive Compensation & Equity: We offer a top-tier salary and a meaningful stock option plan. We want you to have a real stake in what you're building.
3. A Lean, Expert Team: You'll work directly with the founders in a fast-paced environment, free from corporate bureaucracy.
4. Hybrid Work Model: We think that being in the same room often boosts productivity, so we want you in the office at least 2 days per week. But we have no schedule. You decide when to work to maximise your productivity.

If you're an engineer who is obsessed with the generative space and wants to apply your skills to hard problems with real-world impact, we would love to hear from you.

Our interview process is straightforward:

1. You share your GitHub or papers + CV
2. A chat with the founder
3. A technical deep-dive