How to craft a job brief that attracts top talent?
The job title should be clear and precise to attract the right candidates.
To attract a small candidate pool, use specialized job titles. This is useful when you need candidates with very specific expertise.
Computer Vision Research Scientist
Deep Learning Engineer (Computer Vision)
Medical Imaging Engineer
Use these when the role requires deep expertise in a niche area. For example, if your company is focused on medical imaging, a specialized title like “Medical Imaging Engineer” will attract candidates with specific experience in that domain.
To attract a large candidate pool, use broader job titles. This helps attract candidates with a more general skill set.
Computer Vision Engineer
Machine Learning Engineer
Data Scientist (Computer Vision)
Use broader titles when you want to attract candidates who can grow into the role or have transferrable skills. For example, “Machine Learning Engineer” might attract a larger pool of candidates with general AI expertise who can transition into computer vision.
The job summary should provide a high-level overview of the role, the company, and the impact the role will have on the organization. It should be enticing enough to grab the attention of top talent.
A detailed list of responsibilities and requirements helps candidates understand what is expected of them. Include both technical skills (hard skills) and non-technical skills (soft skills).
Top talent seeks more than just a job; they want growth and a supportive culture. Highlighting your company’s culture and benefits can make your job description stand out.
Encourage candidates to apply by including a call to action at the end of the job description. Make it easy for them to understand how to apply and what the next steps are.
Sample job description for {role_name}
Job Title: Computer Vision Engineer
Job Summary: We are seeking a skilled Computer Vision Engineer to join our team and help develop cutting-edge visual algorithms and AI models. As part of our AI division, you will work on image processing, object detection, and machine learning techniques to build applications for industries such as healthcare, technology, and research. This role offers the chance to work on innovative projects and be part of a collaborative, forward-thinking team.
Requirements:
Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related field.
2+ years of experience working with computer vision technologies and machine learning algorithms.
Proficiency in Python and frameworks such as OpenCV, TensorFlow, or PyTorch.
Strong understanding of computer vision algorithms like image classification, object detection, and facial recognition.
Experience in image processing and deep learning.
Familiarity with cloud-based services for deploying AI models (AWS, Azure, Google Cloud).
Responsibilities:
Develop and optimize computer vision algorithms for various real-world applications.
Design and implement image processing techniques to improve the accuracy and performance of models.
Collaborate with data scientists and machine learning engineers to build and deploy deep learning models.
Integrate computer vision solutions into production systems and continuously improve them based on feedback and performance metrics.
Work closely with cross-functional teams to understand project requirements and deliver solutions aligned with business goals.
Stay up-to-date with the latest trends and advancements in computer vision and machine learning technologies.
Must-Have:
Proficiency in computer vision libraries such as OpenCV, as well as machine learning frameworks like TensorFlow and PyTorch.
Hands-on experience in developing and deploying deep learning models for tasks such as object detection, image segmentation, and pattern recognition.
Strong problem-solving skills with the ability to handle complex visual data.
Experience with programming languages such as Python, C++, or MATLAB.
Familiarity with working in cloud environments for AI model deployment.
Soft Skills:
Strong problem-solving and analytical thinking.
Attention to detail, especially when handling large datasets and complex algorithms.
Excellent communication skills to convey technical concepts to non-technical team members.
Research skills to keep up with the latest advancements in AI and computer vision.
