Vision-Language Models and Generative AI

1 week ago

Bengaluru, Karnataka, India Bosch Global Software Technologies Full time ₹ 8,00,000 - ₹ 24,00,000 per year

Job Description

Roles & Responsibilities:

Conduct deep research in:

  • Vision-Language and Multimodal AI for perception and semantic grounding
  • Cross-modal representation learning for real-world sensor fusion (camera, lidar, radar, text)
  • Multimodal generative models for scene prediction, intent inference, or simulation
  • Efficient model architectures for edge deployment in automotive and factory systems
  • Evaluation methods for explain ability, alignment, and safety of VLMs in mission-critical applications
  • Spin newer research directions and drive AI research programs for autonomous driving, ADAS, and Industry 4.0 applications.
  • Create new collaborations within and outside of Bosch in relevant domains.
  • Contribute to Bosch's internal knowledge base, open research assets, and patent portfolio.
  • Lead internal research clusters or thematic initiatives across autonomous systems or industrial AI.
  • Mentor and guide research associates, interns, and young scientists.

Qualifications

Educational qualification:

Ph.D. in Computer Science / Machine Learning / AI / Computer Vision or equivalent

Experience:

8+ years (post PhD) in AI related to Vision and Language modalities, excellent exposure and hands on research in GenAI, VLMs, Multimodal AI, or Applied AI Research.

Mandatory/requires Skills:

Deep expertise in:

  • Vision-Language Models (CLIP, Flamingo, Kosmos, BLIP, GIT) and multimodal transformers
  • Open- and closed-source LLMs (e.g., LLaMA, GPT, Claude, Gemini) with visual grounding extensions
  • Contrastive learning, cross-modal fusion, and structured generative outputs (e.g., scene graphs)
  • PyTorch, HuggingFace, OpenCLIP, and deep learning stack for computer vision
  • Evaluation on ADAS/mobility benchmarks (e.g., nuScenes, BDD100k) and industrial datasets
  • Strong track record of publications in relevant AI/ML/vision venues
  • Demonstrated capability to lead independent research programs
  • Familiarity with multi-agent architectures, RLHF, and goal-conditioned VLMs for autonomous agents

Preferred Skills:

Hands-on experience with:

  • Perception stacks for ADAS, SLAM, or autonomous robots
  • Vision pipeline tools (MMDetection, Detectron2, YOLOv8) and video understanding models
  • Semantic segmentation, depth estimation, 3D vision, and temporal models
  • Industrial datasets and tasks: defect detection, visual inspection, operator assistance
  • Lightweight or compressed VLMs for embedded hardware (e.g., in vehicle ECUs or factory edge)
  • Knowledge of reinforcement learning or planning in embodied AI context
  • Strong academic or industry research collaborations
  • Understanding of Bosch domains and workflows in mobility and manufacturing

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