AI Engineer

About this position

This is a remote position. You'll own generative AI pipelines end to end — from selecting and fine-tuning foundation models to deploying them at scale. Our engineering culture is direct: we prototype fast, we measure everything, and we ship. If you're looking for a place where your work reaches millions, this is it.

Key Responsibilities

• Design, build, and maintain generative AI pipelines — image, video, and multimodal — that power features across our consumer apps.
• Fine-tune and evaluate foundation models on proprietary data to push output quality beyond what off-the-shelf models can achieve.
• Own the full lifecycle of an AI feature: prototype → evaluation → productionisation → monitoring and continuous improvement.
• Collaborate with product and design to translate UX requirements into technically feasible generative experiences.
• Identify and benchmark new open-source and API-based models; ship improvements when a better solution exists.
• Optimise inference pipelines for latency and cost at scale.
• Stay current with Hugging Face releases and the wider generative AI ecosystem.

Requirements & Skills

• Production-grade Python and PyTorch — you're comfortable debugging CUDA OOM errors and reasoning about tensor shapes.
• Deep familiarity with diffusion models — Stable Diffusion architecture, ControlNet, LoRA / DreamBooth fine-tuning — and how to adapt them for product use cases.
• Experience deploying models as scalable inference endpoints: containerisation, batching strategies, GPU cost management.
• Understanding of LLM workflows: prompt engineering, function calling, retrieval-augmented generation, and evaluation harnesses.
• Solid software-engineering fundamentals — clean code, version control, code review, and automated testing.