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Niche diffusion/VLM research and robotics edge deployment significantly reduce applicant competition.
Highly specialized ML research for robotics and diffusion models limits transferability across industries.
Explicit 3+ years, publication record, and mandatory deep-learning and edge deployment skills create high shortlisting rigidity.
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Develop and deploy diffusion-based generative models for photorealistic simulations and defect synthesis on dynamic construction sites.
Architect and train Vision-Language Models connecting textual work orders, CAD plans, and sensor data for pixel-level understanding.
Lead end-to-end AI model lifecycle including auto-annotation pipelines, model optimization for edge devices, and hand-off to perception and control teams.
3+ years in deep-learning R&D or advanced degree (Ph.D./M.S.) in CS, EE, Robotics or related field with strong publication record.
Expertise in diffusion models (e.g., DDPM, LDM, ControlNet) and multimodal transformers / VLMs (e.g., CLIP, BLIP-2, LLaVA, Flamingo).
Proven experience with large-scale data-centric AI workflows: active learning, pseudo-labeling, weak supervision.
Advanced skills in Python, PyTorch (or JAX), experiment tracking, scalable training frameworks, and familiarity with edge-AI runtimes and CUDA/C++ performance tuning.
Experienced in research and production of real-time AI systems for robotics or vision-language applications requiring model compression and edge deployment.
Skilled in designing scalable annotation and training workflows for multimodal data integration in complex environments.
Capable of independently owning full AI research projects from problem definition to production handoff, with technical leadership including mentorship and publication contributions.