Machine Learning Engineer (Diffusion / Vision) (Remote)

Transform Visual Models into Real-World Applications We’re building AI systems for a global audience. We are living in an era of AI transition - this new project team will be focusing on building applications to enable more real world impact and highest usage for the world.

This role is a global role with remote work arrangement. You’ll work closely with regional teams across product, engineering, operations, infrastructure and data to build and scale impactful AI solutions. Why This Role Matters You’ll fine-tune state-of-the-art models, design evaluation frameworks, and bring AI features into production. Your work ensures our models are not only intelligent, but also safe, trustworthy, and impactful at scale. What You’ll Do Fine-tune & Adapt – Train and customize diffusion models (SDXL, Flux, Stable Diffusion variants) using LoRA, DreamBooth, and other parameter-efficient methods. Curate Datasets – Build, clean, and annotate large-scale image datasets with captioning, tagging, and NSFW filtering for safe and aligned generation. Evaluate & Align – Develop pipelines to measure fidelity, diversity, style adherence, and safety across generated outputs. Optimize Performance – Apply GPU memory optimization, latent diffusion tricks, and distributed training for efficient scaling. Deploy & Monitor – Ship diffusion-powered features into production with monitoring for drift, latency, and quality. Collaborate & Deliver – Work with product and design to integrate generative vision capabilities into user experiences. What Is It Like Likes ownership and independence Believe clarity comes from action - prototype, test, and iterate without waiting for perfect plans. Stay calm and effective in startup chaos - shifting priorities and building from zero doesn’t faze you. Bias for speed - you believe it’s better to deliver something valuable now than a perfect version much later. See feedback and failure as part of growth - you’re here to level up. Possess humility, hunger, and hustle, and lift others up as you go. Requirements 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. What You’ll Get Flat structure & real ownership Full involvement in direction and consensus decision making Flexibility in work arrangement High-impact role with visibility across product, data, and engineering Top-of-market compensation and performance-based bonuses Global exposure to product development Lots of perks - housing rental subsidies, a quality company cafeteria, and overtime meals Health, dental & vision insurance Global travel insurance (for you & your dependents) Unlimited, flexible time off Our Team & Culture We’re a densed, high-performance team focused on high quality work and global impact. We behave like owners. We value speed, clarity, and relentless ownership. If you’re hungry to grow and care deeply about excellence, join us. About Bjak BJAK is Southeast Asia’s #1 insurance aggregator with 8M+ users, fully owned by its employees. Headquartered in Malaysia and operating in Thailand, Taiwan, and Japan, we help millions of users access transparent and affordable financial protection through Bjak.com. We simplify complex financial products through cutting-edge technologies, including APIs, automation, and AI, to build the next generation of intelligent financial systems.

If you're excited to build real-world AI systems and grow fast in a high-impact environment, we’d love to hear from you.