Video Research Intern - Hybrid, Paid, Mentoring

MediaKind; Rennes; Hybrid; EUR 13,000 - 17,000; Full time

Benefits offered by this job

Paid internship

Public transport reimbursement 50%

Meals 50% meal allowance

Job summary

MediaKind is offering a six-month Master’s-level internship in Rennes, France, focused on applied video research. You will work under close supervision while owning a well-defined piece of work, from literature review to experimentation and a shareable result.

You will set up experiments with codecs and datasets, build reproducible evaluation tools, and contribute to technical reports and internal presentations with mentorship from senior researchers.

Qualifications

• Hands-on experience in machine learning or deep learning, ideally on image or video problems, using Python and a framework such as PyTorch or TensorFlow.
• Understanding of image/video processing and compression fundamentals (e.g. filtering, transform coding, motion compensation, rate-distortion).
• Ability to read, critically assess and summarize research papers.
• Clear written and spoken English (French is a plus).

Responsibilities

• Run structured literature and state-of-the-art reviews and summarize findings for the team.
• Set up and run experiments with open-source or proprietary codecs, models and datasets.
• Build and maintain reproducible evaluation, benchmarking and experimentation tools.
• Investigate, develop and test proofs of concept (PoCs) with guidance from senior researchers.
• Analyze results and document them in technical reports and team presentations.
• Contribute to code deliveries, publications or patent disclosures.

Skills

ML/DL

Python

PyTorch

TensorFlow

Literature review

Education

Master's student in Electrical Engineering/CS/Video Processing

Tools

FFmpeg

x264/x265

SVT-AV1

VVenC

VTM

Job description

MediaKind is offering a six-month Master’s-level internship in Rennes, France, focused on applied video research. You will work under close supervision while owning a well-defined piece of work, from literature review to experimentation and a shareable result.

You will set up experiments with codecs and datasets, build reproducible evaluation tools, and contribute to technical reports and internal presentations with mentorship from senior researchers.