Employer description & source details
As a core member of the AI Research team you'll turn cutting-edge, vision-language and diffusion advances into robust real-time systems that see reason and act on dynamic construction sites. 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 lifecycle—problem 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. 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, BLIP-2, LLaVA, Flamingo). Proven success building large-scale data-centric AI workflows—active 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 wi
Extracted skills: python, c++