Revolutionizing combat sports with AI HIT Coach, a cutting-edge combat sports technology startup, is on a mission to enhance fan engagement and empower athlete performance. Utilizing our custom computer-vision technology, we bring live, immersive analytics to fans and provide athletes with unparalleled training tools and performance insights.
About the Role: We are seeking a highly skilled and experienced Computer Vision Engineer to join our dynamic team. The ideal candidate will be responsible for developing and implementing advanced computer vision algorithms, focusing on video analysis and action recognition, using deep learning techniques.
Key Responsibilities:
• Video Analysis and Processing: Develop and optimize algorithms for real-time video analysis.
• Video Action Recognition: Design and develop models for recognizing complex actions and behaviors in videos, using state-of-the-art deep learning methods.
• Design and Implement algorithms for real time Object Detection.
• Design and implement Object Tracking, in video streams, and modify the current ones to tackle the needed tasks.
• Hands on pose estimation techniques.
• Previous experience of Semantic Segmentation
• Core Knowledge in Deep learning models development in the main computer vision tasks: Build, Train, and Evaluate and fine-tune deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformers for various computer vision tasks.
• Experience on working with (Self Supervised learning, Few shot learning) to handle tasks with few labelled data.
• Data Management: Work with large-scale video datasets, including data collection, preprocessing, augmentation, and annotation. Ensure high-quality data for model training and validation.
• Performance Optimization: Optimize models for speed and accuracy. Implement efficient algorithms suitable for deployment on various platforms, including edge devices.
• Research and Development: Stay abreast of the latest developments in computer vision and deep learning. Conduct research to advance the state of the art in action recognition and related fields.
• Cross-functional Collaboration: Work closely with other teams, data scientists, software developers, and product managers, to integrate computer vision capabilities into products and solutions.
• Documentation and Reporting: Maintain thorough documentation of algorithms, experiments, and findings. Prepare reports and presentations for both technical and non-technical stakeholders.
Qualifications:
• Master’s or Ph.D. in Computer Vision, Data Science, Computer Science, Electrical Engineering, or a related field with a focus on computer vision and Deep learning.
• Minimum of 3-5 years of experience in Computer Vision and Deep Learning.
• Proficient in programming languages such as Python, and Deep Learning frameworks like PyTorch (preferred) and TensorFlow,.
• Strong understanding of image and video processing techniques.
• Proven experience in training deep neural networks, particularly for video analysis and action recognition.
• Knowledge of cloud computing platforms (e.g., AWS, GCP, Azure) and containerization technologies (e.g., Docker).
• Familiarity with GPU programming and optimization techniques.
• Excellent problem-solving and analytical skills.
• Strong communication and collaboration abilities.
Desirable Skills:
• Experience with 3D computer vision and augmented reality (AR) technologies.
• Familiarity with edge computing and deploying models on edge devices.
Benefits:
• Competitive salary and benefits package.
• Flexible working hours and remote work options.
• Opportunities for professional development and career growth.
• Collaborative and innovative work environment.
• Equity in the company, offering a stake in our long-term success.
Note: Only shortlisted candidates will be contacted for further steps.
At HIT Coach, we value diversity and equal opportunity. We are committed to creating an inclusive environment for all employees and interns.
We look forward to receiving your application!
HIT Coach team
