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Technical Lead, Machine Learning
5 months ago
Who We Are
The Cisco Security AI team delivers AI products and platform for all Cisco secure products and portfolios so businesses around the world can defend against threats and safeguard the most vital aspects of their business with security resilience. We are passionate about making businesses secure and simplifying security with zero compromise using AI and Machine Learning. Who You Are You are an accomplished Senior Machine Learning Engineer with a track record of leading teams and architecting machine learning solutions that have made a significant impact. You are deeply passionate about machine learning, and you thrive in a leadership role where you can mentor and inspire others. Your ideal attributes include: Technical Leader: You have experience leading and mentoring teams of machine learning engineers and/or data scientists, driving them toward excellence in research, development, and solution deployment. Architectural Vision: You possess a deep understanding of ML learning system architecture, and you can design complex, scalable, and reliable solutions that meet both security and performance requirements. Problem-Specific Model Customization : Experience adapting 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 managing and extracting useful patterns from high-dimensional data spaces, using techniques like dimensionality reduction or specialized network architectures. Proficiency in NLP and LLM: Including production experience with BERT, GPT, or similar, to 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 problems, often combining domain knowledge (from cybersecurity or other domains) and innovative thinking. Model Scalability and Efficiency : Expertise in developing 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 Team Leadership: Lead and mentor teams of ML engineers and data scientists, providing technical guidance, setting strategic direction, and fostering innovation and excellence. Architecture Design: Design and architect complex machine learning systems that meet performance requirements, security and scalability while also ensuring alignment with the overall security strategy. Model Development: Develop and implement advanced ML models and algorithms to tackle difficult security problems, including threat detection, anomaly detection, and risk assessment. Model Training and Evaluation: Own the training, validation, and fine-tuning of ML models using the most current 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 models in production environments. This includes developing and optimizing software frameworks, ensuring smooth model integration, and rigorously testing systems to maintain high reliability and performance standards. Deployment Strategy: Collaborate with software engineering teams to build deployment strategies for ML models into security systems, ensuring scalability, reliability, and efficiency. Documentation and Best Practices: Establish standard processes for machine learning and security operations, and maintain clear documentation of models, data pipelines, and security procedures. Basic Qualifications- 8+ years experience leading technical teams with a portfolio of successful projects.
- 5+ years experience with programming languages such as Python or R, and experience with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- 3+ years experience building real-world ML and AI products.
- BA / BS degree with 7+ years of relevant experience (or) MS degree with 5+ years.
- Expertise in machine learning algorithms, deep learning, and statistical modeling.
- Education in Computer Science, Data Science, Statistics, Computational Linguistics or a related field.
