Lead AI Engineer
Leads research and productionization of diffusion, vision-language, and vision-language-action models for real-time robotic perception on construction sites. The role requires 8+ years of deep-learning R&D or an advanced degree with strong publications, plus expertise in scalable training and edge deployment.
About the job
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 Action Models (VLA) that connect textual work orders, CAD plans, and sensor data to pixel-level understanding.
- Lead auto-annotation pipelines using active learning, self-training, and synthetic data to scale to millions of frames and point clouds with minimal human effort.
- Optimize and compress models using INT8, LoRA, and distillation for deployment on Jetson-class edge devices under ROS 2.
- Own the full lifecycle from problem definition, literature review, and prototyping through offline/online evaluation and production handoff to perception and controls teams.
- Publish internal technical reports and external conference papers.
- Mentor interns and junior engineers.
Requirements
- 8+ years in deep-learning R&D, or a Ph.D./M.S. in CS, EE, Robotics, or a related field with a strong publication record.
- Demonstrated expertise in diffusion models, including DDPM, LDM, and ControlNet.
- Experience with multimodal transformers and VLMs, including CLIP, BLIP-2, LLaVA, or Flamingo.
- Proven success building large-scale data-centric AI workflows involving active learning, pseudo-labeling, and weak supervision.
- Advanced proficiency in Python and PyTorch or JAX.
- Experience with experiment tracking and scalable training using PyTorch Lightning, DeepSpeed, or Ray.
- Familiarity with edge-AI runtimes such as TensorRT and ONNX Runtime.
- Experience with CUDA and C++ performance tuning.
- Strong mathematical foundation in probability, information theory, and optimization.
- Ability to translate theory into production code.
Nice-to-haves
- Synthetic data generation experience with Isaac Sim.
- Experience with robotics perception stacks, including ROS 2, Nav2, MoveIt 2, or Open3D.
Skills
Python, PyTorch, JAX, Diffusion Models, Vision-Language Models, Multimodal Transformers, Active Learning, Pseudo-Labeling, Weak Supervision, TensorRT, Onnx Runtime, CUDA, C++, Ros 2, Isaac Sim
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