Multimodal Vision LLM Engineer

3 days ago

Delhi, Delhi, India 3da163f9-ed57-45e3-aa90-bfee7357ebd0 Full time ₹ 15,00,000 - ₹ 30,00,000 per year

About the Role

We are building the next generation of spatial intelligence where robots and 3D systems understand and interact with the world in real time. As a Multimodal LLM Engineer, you will design, train, and deploy vision-language models that understand detected objects, 3D environments, and dynamic scenes. Your work will enable robots and digital tools to reason about objects, context, safety, and actions—entirely on-device.

You will collaborate closely with perception, robotics, and systems engineers to bring together 3D vision, object detection, and LLM reasoning into a unified real-time intelligence engine.

This is a highly technical role with direct impact on core product capabilities.

Responsibilities

  • Develop and fine-tune multimodal LLMs (vision-language, 3D-language, object-context reasoning).
  • Build pipelines that fuse object detection, 3D data, bounding boxes, and sensor inputs into LLM tokens.
  • Architect models that interpret dynamic scenes, track changes, and deliver contextual reasoning.
  • Implement region-based reasoning, spatial attention, temporal understanding, and affordance prediction.
  • Train and optimize models using frameworks such as LLaVA, Qwen-VL, InternVL, CLIP/SigLIP, SAM, DETR, or custom backbones.
  • Convert raw perception output into structured representations (scene graphs, spatial embeddings).
  • Work with Robotics/Systems teams to integrate LLM reasoning into real-time pipelines (30–60 FPS).
  • Develop scalable data pipelines for multimodal datasets (images, detections, 3D meshes, text descriptions).
  • Perform model evaluation on context understanding, safety judgment, and action recommendation.
  • Collaborate on model compression and deployment for edge devices (Rockchip, Jetson, Apple M-series).

Minimum Qualifications

  • MS or PhD in Computer Science, AI/ML, Robotics, or related field—or equivalent experience.
  • 3+ years experience building deep learning models, including transformers.
  • Hands-on experience with multimodal models (VLMs) or LLM fine-tuning.
  • Strong understanding of one or more:
  • Vision Transformers (ViT, SigLIP)
  • CLIP-style contrastive models
  • LLaVA / BLIP / Qwen-VL / InternVL
  • DETR / SAM / YOLO / 3D perception networks
  • Advanced Python and PyTorch skills.
  • Experience training models with large datasets and distributed systems.
  • Solid understanding of model architecture fundamentals (attention, tokenization, embeddings).

Job Type: Full-time

Pay: ₹1,500, ₹3,000,000.00 per year

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