Stagiaire en vision par ordinateur (contrat de 3 mois, 24h-40h/semaine)

As a Computer Vision Intern at Coactive AI, you will engage in algorithm development by designing and implementing advanced algorithms using foundation model embeddings. You'll focus on proof-of-concept projects and exploratory analyses while gaining valuable experience in working with data analysis and numerical evaluations to support improvements in algorithms.

To be eligible for the Computer Vision Intern position at Coactive AI, candidates should have a background in computer science, engineering, mathematics, or a related field. Proficiency in Python and experience with frameworks such as PyTorch, NumPy, and Scikit-learn are crucial. Moreover, hands-on experience with machine learning algorithms and workflows is preferred.

While interning as a Computer Vision professional at Coactive AI, you will gain practical experience in algorithm development and exposure to scalable production systems. This opportunity provides invaluable insights into real-world applications of AI and allows for collaboration with an international team of experts in the field.

The Computer Vision Intern role at Coactive AI is structured as a three-month internship where you will work 24 to 40 hours per week, depending on your availability. This flexibility allows you to balance your academic commitments while gaining essential hands-on experience in the dynamic field of AI.

Coactive AI offers a competitive compensation for the Computer Vision Intern position, ranging from $24 to $29 per hour, which translates to an estimated annual salary of $50,000 to $60,000 CAD for full-time hours. The exact pay is determined based on your experience and qualifications.

When addressing this question, highlight your grasp of what foundation model embeddings are and their importance in computer vision. Discuss how they can improve algorithm performance by enabling better feature representation from visual data.

Discuss a systematic methodology for data analysis, including data collection, preprocessing, exploratory data analysis, and using the insights to refine algorithms. Provide examples if possible to illustrate your process.

When answering, serve details about your academic or practical experience using Python, especially with libraries like NumPy, Scikit-learn, and PyTorch. Mention specific projects or coursework that showcase your proficiency.

Share a specific instance where you faced a significant challenge in a project, detailing the issue, your thought process in resolving it, and the outcome of your solution. Make sure it relates to algorithms or machine learning.

Emphasize your proactive approach in remaining informed about the latest trends in AI and computer vision, such as following relevant journals, attending conferences, participating in webinars, and engaging with online communities.

Illustrate your familiarity with collaboration tools like Git for version control and platforms like Slack or Zoom for communication. Explain how these tools facilitate productive teamwork in project settings.

Talk about specific projects where you utilized Jupyter notebooks for developing and debugging code. Explain how it provided you with an environment to visualize results and how it improves workflow efficiency.

Clarify your understanding of algorithm development as a process encompassing problem definition, data collection, model selection, training, testing, and fine-tuning. Be ready to discuss a particular algorithm you have worked on.

Convey your motivation for applying at Coactive AI, focusing on its groundbreaking work in visual data analysis. Express your eagerness to contribute to impactful projects and learn from industry pioneers.

Discuss your growth mindset and how you view criticism as a valuable opportunity to improve. Provide examples of how you've implemented feedback in past projects to enhance your work's quality.