Embedded AI Software Engineering Co-op (Spring 2027)

About the role

Bosch Rexroth, part of the Bosch Group, is looking for a motivated Embedded AI Software Engineering Intern/Co-op for the spring 2027 term. The intern works on embedded AI and computer vision applications for mobile machines, optimizing AI models and real-time processing pipelines for deployment on embedded hardware. The role offers hands-on experience developing, deploying, profiling and optimizing AI-powered vision systems on resource-constrained platforms, working closely with the software engineering team.

Key responsibilities

• Develop and optimize embedded AI and computer vision applications for mobile machines with the software development team.
• Deploy, benchmark and optimize AI models for embedded targets, including platforms with hardware AI accelerators.
• Optimize real-time video processing and AI inference pipelines to improve throughput and reduce latency while minimizing CPU, GPU and memory usage.
• Profile and troubleshoot bottlenecks in image acquisition, preprocessing, inference, postprocessing and video output.
• Explore model optimization techniques such as quantization, pruning and hardware-specific acceleration.
• Develop and optimize C/C++ software for embedded Linux and real-time operating systems.
• Integrate AI models with camera systems, sensors and communication interfaces.
• Build testing and benchmarking tools for model accuracy, execution time, memory use and pipeline performance.
• Document architecture, optimization strategies and results, and take part in code reviews and team meetings.

Required profile

• Currently pursuing a bachelor's or master's degree in Computer Engineering, Computer Science, Electrical Engineering or a related technical field.
• Strong analytical, debugging and problem-solving skills, and excellent communication and collaboration abilities.
• U.S. work authorization; future sponsorship is not available.

Required skills

• Familiarity with C/C++ and fundamental understanding of embedded systems.
• Basic knowledge of AI, machine learning or computer vision concepts.
• Familiarity with Linux development environments.
• Preferred: experience deploying AI models on embedded hardware, frameworks such as PyTorch, TensorFlow, TensorFlow Lite or ONNX, and real-time video frameworks such as GStreamer or OpenCV.