BJAK is hiring a Machine Learning Engineer focused on diffusion and generative vision. In this role, you will fine-tune state-of-the-art models, design evaluation frameworks, and bring AI features into production to ensure models are safe, trustworthy, and impactful at scale. You'll work on building applications to enable real-world impact and high usage.

What You'll Do

Train and customize diffusion models (SDXL, Flux, Stable Diffusion variants) using LoRA, DreamBooth, and other parameter-efficient methods.
Build, clean, and annotate large-scale image datasets with captioning, tagging, and NSFW filtering for safe and aligned generation.
Develop pipelines to measure fidelity, diversity, style adherence, and safety across generated outputs.
Apply GPU memory optimization, latent diffusion tricks, and distributed training for efficient scaling.
Ship diffusion-powered features into production with monitoring for drift, latency, and quality.
Work with product and design to integrate generative vision capabilities into user experiences.

What We're Looking For

Strong experience with diffusion models and generative vision (Stable Diffusion, SDXL, Flux, etc.).
Hands-on skills with DreamBooth, LoRA/QLoRA, and fine-tuning methods.
Proficiency with PyTorch (preferred).
Experience in dataset preparation (captioning, tagging, filtering, augmentation).
Knowledge of GPU optimization, latent diffusion, and efficient training techniques.
Strong foundations in software engineering, algorithms, and clean code practices.

Nice to Have

Prior work in style transfer, personalization, or multimodal generation.
Experience with safety alignment in generative vision (NSFW detection, adversarial testing).
Open-source contributions in diffusion or generative vision projects.

Technical Stack

PyTorch
Stable Diffusion
SDXL
Flux
LoRA
QLoRA
DreamBooth

Team & Environment

You will join a flat structure where you have real ownership and full involvement in direction and consensus decision making. You will work closely with regional teams across product, engineering, operations, infrastructure and data.