Computer Vision Engineer

Job Title: Computer Vision Engineer

 

Location:

[Location] (Opportunities for remote/hybrid/flexible work available)

 

Reports to:

Head of R&D, Software Development Manager depending on organisaiton

 

Role Purpose

Join our team at [Company Name], where we push the boundaries of machine learning and computer vision to deliver high-impact AI solutions. As a Computer Vision Engineer, you’ll architect and refine algorithms that power a broad range of AI-driven products. You’ll partner with experts across our organization—spanning research, product, and data science—to bring new ideas to life and ensure our solutions meet rigorous performance standards.

 

Company Overview

[Company Name] is an AI-focused organization working to develop intelligent systems that tackle real-world challenges in fields like healthcare, transportation, and smart manufacturing. With an emphasis on collaboration, we nurture an environment where employees can investigate new concepts, propose innovative solutions, and grow their skill sets. A recognized leader in AI, [Company Name] offers comprehensive professional development, inclusive policies, and opportunities for all team members to make a tangible impact on next-generation products.

 

Key Responsibilities

• Advanced Model Development

  • Design and implement computer vision algorithms for detection, segmentation, tracking, and classification tasks using frameworks such as OpenCV, PyTorch, or TensorFlow.

  • Develop and refine deep learning models (e.g., CNNs, Vision Transformers) to achieve high accuracy and efficient performance.

• Integration and Deployment

  • Collaborate with cross-functional teams (Software Engineering, Research, Data Science) to integrate computer vision models into production systems.

  • Optimize inference performance using GPU acceleration, on-device computation, or hardware-specific optimizations.

• Data Pipeline and Preprocessing

  • Establish scalable pipelines for image and video data, ensuring efficient data ingestion, cleaning, and augmentation.

  • Oversee data labeling and quality assurance processes, collaborating with annotation teams when necessary.

• Research and Experimentation

  • Keep pace with cutting-edge scientific literature in computer vision to identify promising new techniques.

  • Participate in the full lifecycle of model experimentation, from prototyping and hyperparameter tuning to versioning and release.

• Code Quality and Documentation

  • Write clear, well-documented, and maintainable code.

  • Maintain thorough documentation for models, experiments, and system designs to facilitate knowledge transfer across the team.

• Performance Monitoring and Iteration

  • Define key metrics for model evaluation (precision, recall, latency, etc.) and track them over time.

  • Investigate and address issues related to model drift or performance degradation.

• Collaboration and Mentorship (as applicable)

  • Provide technical guidance and best practices to junior team members or interns.

  • Participate in regular team meetings to share insights, propose new ideas, and offer constructive feedback on collective projects.

• Budget & Resource Allocation

  • Manage departmental budgets, project timelines, and resource planning. [quantify this information if possible]

  • Provide clear guidance on workload priorities, ensuring optimal utilization of both in-house and external resources.

 

Required Skills and Qualifications

• Educational Background

  • Bachelor’s or Master’s degree (or equivalent experience) in Computer Science, Electrical Engineering, or a related field.

  • Coursework or specialized training in machine learning, deep learning, or a similar domain.

• Technical Proficiency

  • Demonstrable experience in Python or C++ with strong software engineering practices (version control, code reviews).

  • Hands-on expertise with deep learning frameworks (e.g., PyTorch, TensorFlow) and computer vision libraries (e.g., OpenCV).

  • Solid understanding of convolutional neural networks, image processing, and techniques like object recognition and segmentation.

• Machine Learning Fundamentals

  • Familiarity with classical ML approaches (SVMs, Random Forests) alongside deep learning methods.

  • Experience fine-tuning pre-trained models on custom datasets.

• Performance Optimization

  • Knowledge of GPU acceleration (e.g., NVIDIA CUDA) or hardware-specific optimizations.

  • Insights into quantization, pruning, or other model compression methods.​

• Research and Experimentation

  • Keep pace with cutting-edge scientific literature in computer vision to identify promising new techniques.

  • Participate in the full lifecycle of model experimentation, from prototyping and hyperparameter tuning to versioning and release.

• Code Quality and Documentation

  • Write clear, well-documented, and maintainable code.

  • Maintain thorough documentation for models, experiments, and system designs to facilitate knowledge transfer across the team.

• Performance Monitoring and Iteration

  • Define key metrics for model evaluation (precision, recall, latency, etc.) and track them over time.

  • Investigate and address issues related to model drift or performance degradation.

• Collaboration and Mentorship (as applicable)

  • Provide technical guidance and best practices to junior team members or interns.

  • Participate in regular team meetings to share insights, propose new ideas, and offer constructive feedback on collective projects.

• Budget & Resource Allocation

  • Manage departmental budgets, project timelines, and resource planning. [quantify this information if possible]

  • Provide clear guidance on workload priorities, ensuring optimal utilization of both in-house and external resources.

 

Required Skills and Qualifications

• Educational Background

  • Bachelor’s or Master’s degree (or equivalent experience) in Computer Science, Electrical Engineering, or a related field.

  • Coursework or specialized training in machine learning, deep learning, or a similar domain.

• Technical Proficiency

  • Demonstrable experience in Python or C++ with strong software engineering practices (version control, code reviews).

  • Hands-on expertise with deep learning frameworks (e.g., PyTorch, TensorFlow) and computer vision libraries (e.g., OpenCV).

  • Solid understanding of convolutional neural networks, image processing, and techniques like object recognition and segmentation.

• Machine Learning Fundamentals

  • Familiarity with classical ML approaches (SVMs, Random Forests) alongside deep learning methods.

  • Experience fine-tuning pre-trained models on custom datasets.

• Performance Optimization

  • Knowledge of GPU acceleration (e.g., NVIDIA CUDA) or hardware-specific optimizations.

  • Insights into quantization, pruning, or other model compression methods.

• Data Handling and Experimentation

  • Experience with large-scale image and video datasets.

  • Strong background in designing and analyzing experiments, using tools like Python notebooks, MLflow, or similar platforms.

• Analytical Skills

  • Ability to dissect complex algorithms and data structures to troubleshoot bottlenecks or errors.

  • Effective problem-solving strategies to handle ambiguous or evolving project requirements.

• Team and Communication Skills

  • Strong interpersonal skills, with an ability to communicate technical concepts to varied audiences.

  • Collaborative mindset, comfortable working in multi-disciplinary teams spanning research and production.
     

Preferred Qualifications

 

• Hands-on exposure to cloud-based AI services (AWS, Azure, GCP) for end-to-end ML pipelines.

• Familiarity with advanced topics like 3D vision, SLAM (Simultaneous Localization and Mapping), or multisensor fusion.

• Contributions to open-source computer vision libraries or published research in relevant fields.

• Experience using tools like Docker, Kubernetes, or other container/orchestration platforms to streamline development and deployment.

 

Use these details to craft a job posting that appeals specifically to Computer Vision Engineers eager to work in an AI-driven environment. Emphasize opportunities for professional development, highlight the uniqueness of your project goals, and maintain a clear, concise structure for easy readability. By demonstrating your company’s commitment to growth, collaboration, and cutting-edge work, you’ll attract skilled candidates who share your passion for AI.

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Perks and Benefits:

Clearly outline the benefits and perks of the role.

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How to Apply:

End with a strong call to action encouraging candidates to apply. Include a direct link to the application page and provide contact information for further queries.

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Please ensure each job description includes all relevant information in compliance with local, state, and national laws. This includes:

 

• Salary Information: Provide a clear salary range to maintain transparency and meet legal requirements.

• Privacy Policies: Protect candidate privacy by following all applicable data protection and privacy laws.

• Equality & Non-Discrimination: Include an equal opportunity statement to uphold our commitment to a diverse, inclusive workplace that does not discriminate based on race, gender, age, disability, or any other protected characteristic.

• Accessibility: Make reasonable accommodations available for candidates with disabilities and include information on how they can request assistance throughout the hiring process.

• Environmental and Social Responsibility: If your company has sustainability initiatives or community engagement programs, mentioning them briefly can attract candidates who prioritize working for socially responsible employers.

• Transparent Hiring Process: Briefly explain the hiring process (e.g., “Our interview process typically includes three stages: an initial screening, a technical interview, and a final interview”) to help candidates know what to expect.