Gen AI Model Artist

Trains, fine-tunes, and deploys generative AI models at DNEG to create hyperrealistic synthetic media, focusing on identity preservation, controllability, realism, and temporal consistency for sequences.

Location

London, United Kingdom

Compared with 15 other Research roles: Common across similar roles Unique to this role

What you'll do

Responsibilities

• Train and fine-tune GenAI models to generate hyperrealistic synthetic media (image and/or video) Common
• Curate and prepare datasets for generative training: identity coverage, lighting/pose balance, captioning, filtering, de-duplication
• Improve realism and likeness through fine-tuning strategy (adapters/LoRA-style approaches, full fine-tunes) and systematic evaluation
• Diagnose generative failure modes: identity drift, artifacts, texture issues, uncanny results, overfitting, conditioning sensitivity Unique
• Manage multiple training runs with prompt/eval sets, side-by-side comparisons, regression checks, and experiment logs
• Collaborate with R&D to innovate GenAI training techniques; monitor inference speed, VRAM, throughput, and consistency

What you'll need

Experience

• Proven experience with generative model training/fine-tuning (Stable Diffusion family or similar diffusion/GAN/transformer models) Common
• Familiarity with UNIX/Linux environments and GPU training workflows
• Critical eye for facial likeness and visual quality; able to judge and explain issues clearly Unique
• Excellent teamwork, communication, and problem-solving; calm under pressure Unique
• Nice to have: VFX or compositing experience (Nuke), Python + ML engineering, face-swap systems experience Unique

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