Machine Learning Engineer – Generative Imaging

Our client is seeking a Machine Learning Engineer – Generative Imaging to take technical ownership of the model layer for an AI-powered image production platform. Platform engineering is already covered, including APIs, cloud infrastructure, deployment, and application architecture. This role is specifically focused on generative image models and applied machine learning.

The next version of the platform is being designed as a model-agnostic, portable system built on open-weight foundation models rather than being tightly coupled to a single proprietary model stack. The goal is to preserve our client’s proprietary production intelligence, workflows, evaluation logic, and domain-specific behavior while maintaining the ability to benchmark, replace, and adopt stronger underlying models as the market evolves. The system should not require a complete rebuild whenever a model is updated.

The ideal candidate will have deep experience with:

• PyTorch
• Diffusion and flow-based models
• LoRA and model fine-tuning
• Image conditioning and editing
• Multimodal vision
• Model evaluation
• Photorealism, fidelity, and visual consistency
• Open-weight and open-source image models

This person should be comfortable evaluating emerging architectures quickly and helping design an abstraction layer that makes the platform portable across different models.

This is not a pure research role or a generic LLM engineering position. Our client needs a hands-on builder with strong product instincts who can work directly with company leadership to translate real-world visual production challenges into technical solutions and materially improve image quality based on customer needs.

Startup experience is a plus, as is experience working with generative media, computer vision, or advanced imaging technologies.

The key qualification is the ability to own and improve the generative-image intelligence behind the platform while helping build a durable, model-agnostic architecture that remains portable as foundation models evolve.