Research Scientist- Robotics AI

As a Research Scientist- Robotics AI, you contribute to research projects at the forefront of the ADAS/AD industry. Key responsibilities include:
Conduct research and engineering 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.
Push the boundaries in AI based Robotics, incorporating advancements in large vision-language-(action) models to aid reasoning capabilities and explainability.
Collaborate cross-functionally with global research and engineering teams to ensure seamless technology transfer and system integration.
Implement research results to solve real-world challenges, ensuring high-quality system integration within Bosch's existing platforms.
Stay at the forefront of innovation by actively engaging with academic and industry communities through conferences, workshops, and technical events.
Document and disseminate research findings through high-caliber publications and/or patent submissions.
Basic Qualifications
Ph.D. in Computer Science, Robotics or a related discipline or Master's degree with >= 2 years industry experience after graduation.
A minimum of 3 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 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).
A strong portfolio of publications in premier machine learning, deep learning, robotics and computer vision journals and conferences.
Preferred Qualifications
Experience with real-world product development and deployment of autonomous systems.
Hands-on experience building 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.

Employment type
Full-time

Work arrangement
Hybrid