Lead AI Engineer

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 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.

Requirements
Qualifications & Skills

• 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 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 with synthetic data generation in Isaac Sim or robotics perception stacks
(ROS2, Nav2, MoveIt 2, Open3D).