Lead AI Engineer

Responsibilities:

• Research and innovate diffusion-based generative models for photorealistic wall-surface simulation, defect synthesis, and domain adaptation.
• Architect and train Vision-Language Models (VLMs) and Vision-Language Alignment (VLA) objectives that connect textual work orders, CAD plans, and sensor data to pixel-level understanding.
• Lead development of auto-annotation pipelines (active learning, self-training, synthetic data) that scale to millions of frames and point clouds with minimal human effort.
• Optimise and compress models (INT8 LoRA, distillation) for deployment on Jetson-class edge devices under ROS 2
• Own the full lifecycle: problem definition, literature review, prototyping, offline/online evaluation, and production hand-off to perception and controls teams.
• Publish internal tech reports and external conference papers; mentor interns and junior engineers.

Requirements:

• 3+ years in deep-learning R& D or Ph. D. /M. S. in CS, EE, Robotics or related field with strong publication record.
• Demonstrated expertise in diffusion models (DDPM, LDM, ControlNet) and multimodal transformers / VLMs (CLIP, DINO, LLaVA, Flamingo).
• Proven success building large-scale data-centric AI workflows: active learning, pseudo-labelling, weak supervision.
• Advanced proficiency in Python, PyTorch (or JAX), experiment tracking, and scalable training (PyTorch Lightning, DeepSpeed, Ray).
• Familiarity with edge-AI runtimes (TensorRT, ONNX Runtime) and CUDA / C++ performance tuning.
• Strong mathematical foundation (probability, information theory, optimisation) and ability to translate theory into production code.
• Bonus: experience with synthetic data generation in Isaac Sim, or robotics perception stacks (ROS2 Nav2 MoveIt 2 Open3D).