About the Role :

As a core member of the AI Research team, you will transform cutting-edge vision-language and diffusion-model research into robust, real-time systems capable of perceiving, reasoning, and acting in dynamic construction environments.

Key Responsibilities :

- Research & 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 Action Models (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.

- Optimize and compress models (INT8, LoRA, distillation) for deployment on Jetson-class edge devices under ROS 2.

- Own the full lifecycleproblem definition, literature review, prototyping, offline/online evaluation and production hand-off to perception & controls teams.

- Publish internal tech reports and external conference papers; mentor interns and junior engineers.

What we are looking for :

- 8+ 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, BLIP-2, LLaVA, Flamingo).

- Proven success building large-scale data-centric AI workflowsactive learning, pseudo-labeling, 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, optimization) 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).