As a Research Engineer for ADAS AI Systems, you contribute to research projects at the forefront of the ADAS/AD industry. Key responsibilities include:

• Perform engineering tasks to support research in core AI and machine learning fields to enable Embodied AI (including computer vision, autonomous planning, open-world learning, and so on) for related business domains of ADAS/AD, industrial automation, robotics etc. These tasks include the development and training of AI-planners (using imitation and reinforcement learning), data-preparation tasks (auto-labeling, filtering, etc.), integrating models into a system context, setting up evaluation and benchmarking pipelines, creating demonstrations and visualizations and more.
• Support the team in pushing the boundaries in (modular) end-to-end perception and planning for ADAS/AD, and large vision-language-(action) models.
• Collaborate with a global team to transfer cutting-edge research findings to Bosch's operational units.
• Implement research results to solve real-world challenges, ensuring high-quality system integration within Bosch's existing platforms.
• Stay abreast of the latest technological advancements and market trends by attending academic conferences, technical events, and seminars.
• Document and disseminate research findings through high-caliber publications and/or patent submissions.

Basic Qualifications

• Master’s degree in computer science, robotics or a related discipline or undergraduate degree with at least 2 years of industry experience after graduation.
• A minimum of 2 years of R&D experience, or an equivalent graduate research background, primarily in AI technologies including Computer Vision and Robotic or Automotive Motion and Behavioral Planning.
• Proficiency in one or more programming languages commonly used in machine learning (e.g., Python, C++, Rust).
• Strong software engineering competencies.
• Strong interpersonal, communication, and teamwork capabilities.
• Knowledge of major machine learning frameworks like TensorFlow or PyTorch.
• Hands-on experience in reinforcement learning for behavior or motion planning or other applicable contexts and familiarity with common RL techniques (e.g. PPO, DQN, DDPG).

Preferred Qualifications

• 1 year industry experience in relevant fields after graduation.
• Experience with real-world product development and deployment of autonomous systems.
• Hands-on experience building, training and applying multimodal transformer-based sequence-to-sequence models, especially multimodal vision-language-action models.
• Hands-on experience in computer vision and deep learning, with work in any of the following areas: multimodal transformers, multimodal language models, diffusion models, NeRF, gaussian splatting, object detection / segmentation, 3D scene understanding, sensor calibration, SfM, voxel/BEV grid-based feature representation.