Staff Machine Learning Engineer

About the position

Fortinet is seeking a skilled and innovative Staff Machine Learning Engineer to join our FortiCNAPP cloud cybersecurity team. As a Staff Machine Learning Engineer, you will work closely with data scientists, cybersecurity analysts, and software developers to design, develop, and deploy machine learning models that assess and mitigate risk across complex cloud environments. Your focus will include building models to identify potential security threats and quantify risk, empowering our clients to make informed decisions on their cloud security posture.

Responsibilities

• Develop probabilistic models and statistical frameworks to assess security risk in cloud environments, integrating data from network logs, user behaviors, and threat intelligence to provide actionable risk assessments.
• Design, train, and evaluate machine learning models for threat detection, anomaly detection, and other cybersecurity applications, particularly within cloud-based infrastructure.
• Implement machine learning models in production environments, focusing on model optimization for high performance and scalability, especially in cloud-based or hybrid environments.
• Drive innovation in cybersecurity by developing novel machine learning applications, with the potential to be patented, published, and presented at industry-leading events.
• Work alongside threat analysts to incorporate domain expertise into model features, ensuring model relevance to real-world cyber threat scenarios.
• Develop automated tools for model training, evaluation, and monitoring to streamline processes and maintain model performance over time.
• Participate in code reviews, provide feedback, and mentor junior engineers to foster best practices in the team.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or other quantitative fields. PhD is a plus.
• 6+ years of experience in machine learning, data science, or a related field, with at least 2 years in cybersecurity or cloud-based environments.
• Proficiency in Python, including common ML libraries such as PyTorch, TensorFlow, and Scikit-Learn.
• Experience with probabilistic and statistical modeling for risk assessment, anomaly detection, and classification algorithms.
• Strong understanding of data preprocessing, feature engineering, and data pipeline design.
• Knowledge of cloud computing platforms (AWS, Azure, GCP) and familiarity with securing and monitoring cloud infrastructure.
• Familiarity with containerization (Docker, Kubernetes) and deploying ML models in production.
• Experience with big data processing platforms and frameworks (Snowflake, Spark) is a plus.
• Solid understanding of cybersecurity principles, including network security, malware analysis, incident response, and risk assessment in cloud environments.
• Ability to analyze large, complex datasets and develop actionable insights and recommendations, particularly within a cloud context.
• Strong problem-solving skills with the ability to handle ambiguity and propose innovative solutions to complex cybersecurity challenges.
• Excellent written and verbal communication skills; able to explain technical concepts to non-technical stakeholders.

Nice-to-haves

• Familiarity with LLMs to provide model explainability in natural language.
• Experience with real-time data processing or streaming data.
• Familiarity with cybersecurity standards, protocols, and compliance requirements.
• Prior experience working in cross-functional teams within a fast-paced environment.
• Knowledge of adversarial machine learning and techniques to make models robust to adversarial attacks is a plus.

Benefits

• Medical, dental, vision, life and disability insurance
• 401(k)
• 11 paid holidays
• Vacation time
• Sick time
• Comprehensive leave program