Multimodal AI Research Lead

Bosch Global Software TechnologiesRepublic Of India, IN

2 days ago

Job description

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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