Lead AI Engineer - Edge Devices - VLM/Diffusion

datavruti; Bengaluru; On-site; INR 2,600,000 - 4,600,000; Full time

Benefits offered by this job

Restaurant d'entreprise

Indemnités de stage/alternance

Job summary

datavruti in Bengaluru is seeking a senior AI researcher to translate diffusion, vision-language, and robotics research into robust real-time perception for construction sites. You will prototype and evaluate models, plan experiments, and collaborate with perception and controls teams.

The role requires publishing results, mentoring juniors, and delivering production-ready code that scales to millions of frames and sensor streams on edge devices.

Qualifications

• PhD/MS in CS/EE/Robotics or related field with strong publications.
• Strong publication record in diffusion or multimodal AI.
• Experience building large-scale data-centric AI workflows.

Responsibilities

• Research & innovate diffusion-based generative models for applications relevant to construction AI.
• Architect and train VLMs/VLA models linking work orders, CAD plans and sensor data.
• Lead auto-annotation pipelines and scalable data pipelines.
• Optimize and compress models for edge deployment on Jetson-class devices.
• Own full lifecycle from problem definition to production hand-off; publish reports and mentor juniors.

Skills

Diffusion models

Multimodal transformers

Active learning

Python

PyTorch

CUDA

Edge AI

Robotics perception

Education

Ph.D./M.S. in CS/EE/Robotics or related field

Tools

PyTorch

JAX

TensorRT

ONNX Runtime

ROS2

MoveIt 2

Open3D

Job description

Hiring for: US-based Construction AI-Robotics company

About the Role

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.

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.

Qualifications & Skills

• 5+ 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).