Research Engineer – Generative AI and Computer Vision
Company: Bosch Group
Location: bengaluru, , India
Type: Full-time
Posted: 2026-07-27
About this role
Bosch Global Software Technologies Private Limited is a 100% owned subsidiary of Robert Bosch GmbH, one of the world's leading global supplier of technology and services, offering end-to-end Engineering, IT and Business Solutions. With over 27,000+ associates, it’s the largest software development center of Bosch, outside Germany, indicating that it is the Technology Powerhouse of Bosch in India with a global footprint and presence in the US, Europe and the Asia Pacific region.
Role Overview
The Research Engineer – Generative AI and Computer Vision focuses on advancing state-of-the-art Generative AI and deep learning methods for real-world computer vision applications. The role combines applied research, experimentation, and engineering to develop robust AI solutions for image understanding, synthetic data generation, visual inspection, and continuous model monitoring.
The role will contribute to applied research and development of scalable AI capabilities spanning the full computer vision lifecycle—from data preparation and model development to deployment, monitoring, and continuous improvement. The work will address practical challenges such as limited or imbalanced training data, rare events, changing image conditions, data drift, and model-performance degradation in operation.
This role offers the opportunity to work at the intersection of advanced AI research and real-world deployment, translating novel Generative AI and computer vision methods into reliable and scalable solutions across a range of industrial and business applications.
Roles & Responsibilities :
· Generative AI and Synthetic Data:Research, design, and implement Generative AI approaches for creating realistic and diverse synthetic image datasets. Explore diffusion models, generative adversarial networks, image-to-image generation, controllable generation, and related techniques to address limited data, rare defects, class imbalance, and long-tail inspection scenarios.
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