Machine Learning Engineer

We are seeking a skilled and innovative Machine Learning Engineer to join our cybersecurity team. As a mid-senior level 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 probabilistic and statistical models to identify potential security threats and quantify risk, empowering our clients to make informed decisions on their cloud security posture.

Key Responsibilities

• Risk Modeling and Explainability: 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.[1] 
• Model Development: Design, train, and evaluate machine learning models for threat detection, anomaly detection, and other cybersecurity applications, particularly within cloud-based infrastructure.
• Data Pipeline Engineering: Collaborate with data engineers to develop and optimize data pipelines that process, clean, and transform raw cybersecurity data into formats suitable for machine learning.
• Deployment and Optimization: Implement machine learning models in production environments, focusing on model optimization for high performance and scalability, especially in cloud-based or hybrid environments.
• Research and Prototyping: Stay current on the latest ML techniques and tools; prototype and experiment with new algorithms to continuously enhance our capabilities.
• Threat Analysis Collaboration: Work alongside threat analysts to incorporate domain expertise into model features, ensuring model relevance to real-world cyber threat scenarios.
• Automation and Monitoring: Develop automated tools for model training, evaluation, and monitoring to streamline processes and maintain model performance over time.
• Code Review and Mentorship: Participate in code reviews, provide feedback, and mentor junior engineers to foster best practices in the team.

Required Skills and Qualifications

• Education: Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or other quantitative fields. PhD is a plus.
• Experience: 4+ years of experience in machine learning, data science, or a related field, with at least 2 years in cybersecurity or cloud-based environments.
• Technical Skills:
  • 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.
• Domain Knowledge: Solid understanding of cybersecurity principles, including network security, malware analysis, incident response, and risk assessment in cloud environments.
• Analytical Skills: Ability to analyze large, complex datasets and develop actionable insights and recommendations, particularly within a cloud context.
• Problem Solving: Strong problem-solving skills with the ability to handle ambiguity and propose innovative solutions to complex cybersecurity challenges.
• Communication: Excellent written and verbal communication skills; able to explain technical concepts to non-technical stakeholders.

Preferred Skills

• 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.

Could add "experience with LLMs" since explainability of ML model results across the product is becoming critical, but the job description might be too broad. For risk modeling and threat detection, traditional ML is more important.

This job opening is available to candidates in both the US and Canada.

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For US:

The US base salary range for this full-time position is $160,000-$190,000. Fortinet offers employees a variety of benefits, including medical, dental, vision, life and disability insurance, 401(k), 11 paid holidays, vacation time, and sick time as well as a comprehensive leave program.

Wage ranges are based on various factors including the labor market, job type, and job level. Exact salary offers will be determined by factors such as the candidate's subject knowledge, skill level, qualifications, experience, and geographic location.

All roles are eligible to participate in the Fortinet equity program, Bonus eligibility is reviewed at time of hire and annually at the Company’s discretion.

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For Canada:

The Canada base salary range for this full-time position is expected to be between $100,000-$135,000 annually.  Wage ranges are based on various factors including the labour market, job type, and job level.  Exact salary offers will be determined by factors such as the candidate’s subject knowledge, skill level, qualifications, and experience.

Fortinet strives to provide you and your family with a comprehensive benefits package. Benefits eligibility starts on your first day of hire and comprises of 100% company paid medical, dental, and vision coverage, including a Health Spending Account and a Personal Spending Account that gives you flexibility to spend where you need it the most. Our Employee & Family Assistance Plan (EFAP) offers you and your family access to various services like counseling, legal advice, mental health resources etc. We also provide critical illness, disability, and life insurance, as well as a Group Registered Retirement Savings Plan (RRSP) with a company match to help you save faster for retirement. We offer competitive Paid Time Off and flexible leave policies, including paid health days, to help you take care of yourself and your family members.

All roles are eligible to participate in the Fortinet equity program, Bonus eligibility is reviewed at time of hire and annually at the Company’s discretion.

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Why Join Us:
We encourage candidates from all backgrounds and identities to apply. We offer a supportive work environment and a competitive Total Rewards package to support you with your overall health and financial well-being. Embark on a challenging, enjoyable, and rewarding career journey with Fortinet. Join us in bringing solutions that make a meaningful and lasting impact to our 660,000+ customers around the globe.

 

Fortinet (NASDAQ: FTNT) secures the largest enterprise, service provider, and government organizations around the world. Fortinet empowers its customers with intelligent, seamless protection across the expanding attack surface and the power to take on ever-increasing performance requirements of the borderless network - today and into the future. Only the Fortinet Security Fabric architecture can deliver security without compromise to address the most critical security challenges, whether in networked, application, cloud or mobile environments. Fortinet ranks number one in the most security appliances shipped worldwide and more than 500,000 customers trust Fortinet to protect their businesses.
We are committed to providing reasonable accommodations for all qualified individuals with disabilities. If you require assistance or accommodation due to a disability, please contact us at accommodations@fortinet.com.   Fortinet is an equal opportunity employer. We value diversity in our company, and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, military/veteran status or any other applicable legally protected characteristics in the location in which the candidate is applying.