Hyperreal GenAI Model Artist - Training & Fine-Tuning

Workman Labs; Greater London; Hybrid; GBP 90,000 - 130,000; Full time

Job summary

Workman Labs is seeking a talented engineer to train and fine-tune GenAI models that generate hyperrealistic media. You will curate datasets ensuring identity coverage and balance lighting and pose while maintaining data quality.

You will apply advanced fine-tuning strategies, evaluate results, and diagnose failures like identity drift or artifacts. Collaboration with R&D and robust experiment logging are essential.

Qualifications

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

Responsibilities

• Train and fine-tune GenAI models to generate hyperrealistic media (image and/or video).
• 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.
• 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.

Skills

GenAI training

Linux/UNIX

Facial likeness evaluation

Teamwork & communication

Python ML engineering

Tools

Stable Diffusion

Nuke

LoRA fine-tuning

Python

Job description

Workman Labs is seeking a talented engineer to train and fine-tune GenAI models that generate hyperrealistic media. You will curate datasets ensuring identity coverage and balance lighting and pose while maintaining data quality.

You will apply advanced fine-tuning strategies, evaluate results, and diagnose failures like identity drift or artifacts. Collaboration with R&D and robust experiment logging are essential.