ML Engineer

• Problem-Specific Model Customization: Proven ability to maximise and adapt neural network architectures like GANs (Generative Adversarial Networks), Autoencoders, Attention Mechanisms, and Transformers or developing novel architectures to address unique challenges in different domains (e.g., NLP, computer vision, time series analysis).
• Handling High-Dimensional Data: Experience in managing and extracting useful patterns from high-dimensional data spaces, using techniques like dimensionality reduction or specialized network architectures.
• Proficiency in NLP and LLM is needed, including hands on experience with BERT, GPT, or similar, to effectively derive insights from structured and/or unstructured text data. Building custom real-world production NLP models for tasks like text generation or text summarization is a strong plus.
• Innovative Solution Development: Ability to apply deep learning techniques innovatively to solve complex, non-traditional problems, often combining domain knowledge (from cybersecurity or other domains) and out-of-the-box thinking.
• Model Scalability and Efficiency: Expertise in developing scalable and efficient models that can be deployed in different environments, including understanding trade-offs between model complexity and performance.
• Continuous Learning and Adaptation: Commitment to staying abreast of the latest research and trends in deep learning, continually integrating new findings and techniques into problem-solving approaches.
  

What You Will Do

• Model Development: Develop and implement advanced machine learning models and algorithms to tackle challenging security problems, including threat detection, anomaly detection, and risk assessment.
• Model Training and Evaluation: Lead the training, validation, and fine-tuning of machine learning models, using innovative techniques and libraries. Define and track security-specific metrics for model performance.
• Solution Development: Design and implement robust software systems to integrate, deploy, and maintain advanced machine learning models in production environments. This includes developing and optimizing software frameworks, ensuring seamless model integration, and rigorously testing systems to maintain high reliability and performance standards.
• Deployment Strategy: Collaborate with software engineering teams to design and implement deployment strategies for machine learning models into security systems, ensuring scalability, reliability, and efficiency.
• Documentation and Best Practices: Establish best practices for machine learning and security operations, and maintain clear documentation of models, data pipelines, and security procedures.