Lead AI Engineer
Procore Technologies · Bengaluru
- Experience4–6 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelmid
- Posted2 Sept 2026
About Procore Technologies
Procore Technologies is hiring in Bengaluru in real estate construction. This role looks for around 4+ years of experience.
Skills
- Diffusion models
- Vision-Language Models
- Active learning
- Pseudo-labeling
- Weak supervision
- Python
- PyTorch
- Experiment tracking
- PyTorch Lightning
- DeepSpeed
- Ray
- TensorRT
- ONNX Runtime
- CUDA
- C++
- Optimization
- Probability
- Information theory
The role
A generative AI engineer at a construction technology company researches diffusion models and multimodal transformers for photorealistic surface simulation and robotics perception, building active learning workflows and optimizing edge AI runtimes. The role applies PyTorch, synthetic data generation, and model distillation to production systems.
Full job description
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 Alignment (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 and controls teams.
Publish internal tech reports and external conference papers; mentor interns and junior engineers.
Requirements:
3+ 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 (DDPM, LDM, ControlNet) and multimodal transformers / VLMs (CLIP, DINO, LLaVA, Flamingo).
Proven success building large-scale data-centric AI workflows: active learning, pseudo labeling, and 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).