Are you a problem solver looking for a hands-on internship position with a market-leading company that will help develop your career and reward you intellectually and professionally?

About Analog Devices

Analog Devices, Inc. (NASDAQ: ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at and on and.

At ADI, you will learn from the brightest minds who are here to help you grow and succeed. During your internship, you will make an impact through work on meaningful projects alongside a team of experts. Collaborating with colleagues in an environment of respect and responsibility, you will create connections that will become a part of your professional network.

ADI’s culture values aligned goals, work-life balance, continuous and life-long learning opportunities, and shared rewards. The internship program features various lunch-and-learn topics and social events with other interns and full-time employees.

At ADI, our goal is to develop our interns so they are the first to be considered for full-time roles.

Apply now for the opportunity to grow your career and help innovate ahead of what’s possible.

Job Title: RF & Edge AI Intern (Video Analytics) – 4–6 Month Internship/Working Student (Masters/PhD)

Location: Munich

About the Role

Edge AI systems only matter if they run reliably on real hardware under real constraints. This internship focuses on building and validating video analytics for drone detection and related situational awareness use cases, taking models from dataset to deployment on edge devices. Mentorship and structured check-ins are built in, and you’ll be expected to deliver a working demo plus a clear technical readout by the end of the internship.

What You’ll Work On (Core Responsibilities)

Train, evaluate, and iterate drone‑detection models using video datasets and practical performance targets.

Build video pipelines to handle preprocessing, inference, post‑processing, and event triggering (e.g., tracking, confidence scoring, alert logic).

Deploy optimized models on constrained edge platforms, applying quantization or pruning where appropriate to meet latency, power, and memory limits.

Design and execute benchmarking experiments to measure accuracy, false positives/negatives, robustness to lighting/weather/background clutter, and end‑to‑end latency.

Maintain structured data and experiment tracking to ensure reproducibility (datasets, configs, metrics, model versions).

 Communicate technical findings through concise reports and demos, providing clear recommendations and next steps.

Example Internship Deliverables (4–6 Months)

A working edge demo for drone detection on video (live camera or recorded stream), including an alert/annotation overlay

A measured benchmark report (precision/recall, false alarms, latency, compute footprint) and a proposed improvement plan

A deployment-ready inference package (container/app/script) with documented setup and test procedure

Requirements

Current Masters or PhD student in Electrical/Electronic Engineering, Computer Engineering, Computer Science, or related field (enrolled throughout the internship)

Solid ML foundations, including CNN-based vision models and evaluation metrics (precision/recall, ROC, confusion matrix)

Hands-on experience with at least one ML stack (PyTorch preferred, TensorFlow acceptable)

Experience with video/computer vision tooling (e.g., OpenCV, FFmpeg) and building practical pipelines

Programming ability in Python; C/C++ is a plus for performance-critical edge work

Strong technical communication: able to explain what you tried, what happened, and what it means

Nice to Have

Experience with edge runtimes and optimization (ONNX, TensorRT, TFLite, OpenVINO)

Familiarity with embedded/Linux deployment and profiling (GPU/NPU acceleration, memory/latency profiling). Preferably with NVIDIA GPU environment.

Exposure to detection/tracking architectures (e.g., YOLO-family, SSD, DETR, DeepSORT/ByteTrack)

Experience with dataset curation/labelling strategies and handling class imbalance

Understanding of real-world sensing constraints (camera optics, motion blur, range, occlusion)

What We Offer

Close mentorship from a technical team building real edge systems

A defined project with milestones and an end-of-internship technical readout

Exposure to the full lifecycle: data → model → deployment → benchmarking

A collaborative team environment with strong learning culture

Job Req Type: Internship/CooperativeRequired Travel: No