• Design and prototype ML and deep learning models, with computer vision as the primary domain (object detection, classification, tracking).
• Frame business problems as testable hypotheses and develop proofs-of-concept to validate them, iterating on the results.
• Evaluate and benchmark competing model architectures and pre-trained models to identify the best-fit approach.
• Collaborate with ML Engineers to scale validated prototypes into production systems and stay engaged through deployment.
• Track experiments, manage model and data versioning, and define evaluation metrics to compare approaches objectively.
• Work with product, engineering, and business teams to turn objectives into applied ML solutions.

• Master's degree in a relevant field such as Electrical Engineering, Computer Science, or a related quantitative discipline.
• Hands-on experience developing ML and deep learning models, including demonstrated computer vision work.
• Strong proficiency in Python and a deep learning framework (PyTorch or TensorFlow).
• Experience with computer vision models for object detection, image classification, and tracking.
• Solid grounding in deep learning, traditional computer vision, and classical ML.
• Strong experimentation mindset: benchmarking, ablation studies, and structured evaluation of competing approaches.
• Experience working with large, complex datasets.

• Experience collaborating with ML Engineers to bring models into production.
• Experience with any major cloud platform (Azure, AWS, or GCP) for ML training and deployment.
• Familiarity with Docker and CI/CD pipelines.
• Experience with Hugging Face Transformers, vision transformers, or self-supervised / representation learning.
• Exposure to generative AI: prompt engineering, RAG, LLM frameworks (LangChain, LlamaIndex, Haystack), or LLM fine-tuning.
• Background in gaming, iGaming, e-commerce, or other consumer-facing applications at scale.

• Work on substantial, real-world computer vision and deep learning problems at scale.
• End-to-end involvement, from research and prototyping through to production.
• Opportunities for professional and personal development.
• A collaborative, cross-functional environment with visible impact.

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