How do I apply for the RF & Edge AI Intern (Video Analytics) position at Analog Devices and track my application?

We provide a direct link to the official application portal. You can apply directly here . Additionally, you can easily track this and other applications by clicking the bookmark icon to save it to your YGI Kanban board .

What are the working arrangements and expected duration for the RF & Edge AI Intern (Video Analytics) role?

This position is a full-time, structured as a not disclosed role, with an expected duration of 5 months.

What educational background or degree level is required for the RF & Edge AI Intern (Video Analytics) at Analog Devices?

Candidates are generally expected to be holding or pursuing the following degree levels: Master, PhD.

Does Analog Devices provide visa sponsorship or assistance for international applicants?

Regarding visa assistance and sponsorship for this role, the official status is currently listed as: Not disclosed.

RF & Edge AI Intern (Video Analytics)

Analog Devices

Found 1 month ago

Location

Munich, Germany

Duration (Months)

5 Months

Time

Full-time

Work Mode

Not disclosed

Salary

Not disclosed

Visa Help

Not disclosed

Last Verified

1 month ago

Education

Master
PhD

Skills & Qualifications

Technical Skills

ML foundations
CNN-based vision models
PyTorch
TensorFlow
video/computer vision tooling
OpenCV
FFmpeg
Python
C/C++
edge runtimes
optimization
ONNX
TensorRT
TFLite
OpenVINO
embedded/Linux deployment
profiling
GPU/NPU acceleration
memory/latency profiling
NVIDIA GPU environment
detection/tracking architectures
YOLO-family
SSD
DETR
DeepSORT/ByteTrack
dataset curation/labelling strategies
class imbalance
sensing constraints
camera optics
motion blur
range
occlusion

Soft Skills

problem solver
technical communication

Job Description

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.

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

AI & Machine Learning

Computer Vision

Nice to Haves

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)

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12Open Positions

RF & Edge AI Intern (Video Analytics)

Education

Skills & Qualifications

Technical Skills

Soft Skills

Job Description

Requirements

Related Field

Related Subfield

Nice to Haves