Machine Learning Engineer

LSports is a global leader in sports data, committed to revolutionizing the industry with innovative solutions.

Our focus is sports data collection and analysis, advanced data management, and cutting-edge services such as AI-powered sports tips and high-quality sports visualization. As the sports data landscape grows, we remain at the forefront, delivering real-time solutions.

If you're passionate about sports and technology and want to drive the sports-tech and data industries into the future, we invite you to join our team.

Visual Livescore:

The Visual Livescore group is at the forefront of revolutionizing live sports data, leveraging cutting-edge computer vision techniques to deliver unparalleled real-time live-score events with unmatched accuracy and reliability.

Responsibilities:

• Lead the design and implementation of Deep Learning techniques in Video stream analysis
• Collaborate closely with data scientists and domain experts to operationalize machine learning models into production.
• Mentor junior engineers and provide technical guidance in machine learning best practices.
• Conduct research to keep abreast of the latest advancements in machine learning and artificial intelligence.
• Evaluate the effectiveness of machine learning models through comprehensive validation metrics and make iterative improvements.
  
  

• Minimum of 5 years of experience in machine learning or data science, with a proven track record of deploying machine learning models into production (tensorrt,onnx)
• 4+ years of programming experience in Python.
• 2 years experience in C++ -Must
• Proficiency In CUDA programming
• Proven experience in Computer Vision - Advantaged
• Proficiency in Machine Learning frameworks like TensorFlow, PyTorch
• Strong understanding of algorithms, data structures, and statistical modeling techniques.
• Good understanding of micro-services architecture and design best practices
• At least B.A degree in Computer Science, Mathematics, Statistics,Electronics or a related field