Software Engineering Co-Op - Spring 2027

About the job

We are seeking a motivated and talented Embedded AI Software Engineering Intern/Co-op to join our team for the summer. As an intern, you will work on cutting-edge embedded AI and computer vision applications for mobile machines, with a focus on optimizing AI models and real-time processing pipelines for deployment on embedded hardware.

This role provides hands-on experience developing, deploying, profiling, and optimizing AI-powered vision systems on resource-constrained embedded platforms. You will work closely with our software engineering team to improve inference performance, reduce processing latency, and maximize hardware utilization while maintaining real-time system performance.

Responsibilities

• Collaborate with the software development team to develop and optimize embedded AI and computer vision applications for mobile machines.
• Deploy, benchmark, and optimize AI models for execution on embedded targets, including platforms equipped with hardware AI accelerators.
• Optimize real-time video processing and AI inference pipelines to improve throughput, reduce latency, and minimize CPU, GPU, and memory utilization.
• Profile and troubleshoot on-target performance bottlenecks involving image acquisition, preprocessing, inference, postprocessing, and video output.
• Explore AI model optimization techniques such as quantization, pruning, and hardware-specific acceleration to improve inference efficiency.
• Develop and optimize software components in C/C++ for embedded Linux and real-time operating systems.
• Integrate AI models with camera systems, sensor inputs, and embedded communication interfaces.
• Develop testing and benchmarking tools to evaluate model accuracy, execution time, memory consumption, and end-to-end pipeline performance.
• Document software architecture, optimization strategies, performance results, and implementation details.
• Participate in team meetings, code reviews, and technical discussions.

Minimum Qualifications

• Currently pursuing a bachelor's or master's degree in Computer Engineering, Computer Science, Electrical Engineering, or another relevant technical field.
• Familiarity with C/C++ programming.
• Fundamental understanding of embedded systems and software development.
• Basic knowledge of artificial intelligence, machine learning, or computer vision concepts.
• Familiarity with Linux development environments.
• Strong analytical, debugging, and problem-solving skills.
• Excellent communication and collaboration abilities.

Preferred Qualifications

• Experience deploying AI or computer vision models on embedded hardware.
• Familiarity with AI frameworks and deployment tools such as PyTorch, TensorFlow, TensorFlow Lite, or ONNX.
• Experience optimizing neural networks through quantization, model compression, or hardware-specific inference acceleration.
• Familiarity with embedded AI accelerators, GPUs, or NPUs, including platforms such as Texas Instruments TDA4.
• Experience with real-time video processing frameworks such as GStreamer, OpenCV, or similar technologies.
• Experience with embedded Linux, QNX, or other real-time operating systems.
• Familiarity with performance profiling tools and optimization techniques for CPU, GPU, memory, and system-level bottlenecks.
• Knowledge of image processing, object detection, or semantic segmentation models.
• Experience with embedded communication protocols such as CAN or Ethernet.
• Familiarity with cross-compilation, hardware debugging, and on-target software development.

Indefinite U.S. work authorized individuals only. Future sponsorship for work authorization unavailable.

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Equal Opportunity Employer, including disability / veterans

By submitting an application for employment with Bosch, you represent and warrant that: (a) the work product and materials you submit are your own and reflect your personal experience, skills and qualifications; and (b) you have not used artificial intelligence or automated tools to generate, alter, or enhance application materials or responses in a manner that misrepresents your identity, authorship, experience, capabilities or qualifications.