Machine Learning Engineer

Available Locations: Hybrid - Austin, Champaign, San Francisco, Washington DC or Remote - US
About the department
Cloudflare's Engineering Team is home to some of the industry's top engineers, dedicated to building and scaling innovative software that handles a huge proportion of the Internet. Our team responsible for Bots and Fraud Detection develops cutting-edge solutions to identify and mitigate malicious bot activities, ensuring the security and integrity of online applications. The team collaborates with product and other engineering teams to implement advanced fraud detection mechanisms and employ machine learning techniques to safeguard digital assets from evolving threats in the cybersecurity landscape.
What you'll do
Join Cloudflare's Bot and Fraud Detection team to tackle real-world security challenges, leveraging massive threat intelligence data through data science and machine learning. Design, build, and scale high-performance distributed software for one of Cloudflare's fastest-growing security products. Work with global data exceeding 50 million HTTP requests per second, utilizing a tech stack that includes Go, Python, JavaScript, Rust, Lua, Kafka, Kubernetes, ClickHouse, PostgreSQL, and Jupyter notebook. Make a meaningful impact in detecting and mitigating abusive HTTP L7 layer traffic worldwide.
The candidate should have strong foundational machine learning knowledge. Day to day responsibilities include:

• Collaborate with analysts to understand new attack techniques then design and deploy models to detect these techniques
• Implement Machine Learning models in production systems and monitor their performance in real-time.
• Continuously improve the performance and scalability of existing Machine Learning models.
• Identify, diagnose and resolve issues in production environments
• Contribute to our MLOps Platform to ensure the platform's evolution

Examples of desirable skills, knowledge and experience

• Thorough understanding of machine learning concepts such as what types of models perform best again what types of data
• Desire to see a project through all the way from model creation, training, productionalized deployment, through automated retraining from feedback
• Demonstrated ability to deliver and listen to feedback.
• Proven ability to deliver performance (what) with the right behaviors (how)
• Strong web security fundamentals

Bonus Points
These are things which the role is likely to require; we're happy to help you learn on the job, but prior experience is beneficial

• Experience with deploying Deep Learning models in production systems
• Experience with bots and fraud detection

Compensation
Compensation may be adjusted depending on work location.

• For Colorado-based hires: Estimated annual salary of $137,000 - $167,000.
• For New York City, Washington, and California (excluding Bay Area) based hires:): Estimated annual salary of $154,000 - $188,000
• For Bay Area-based hires: Estimated annual salary of $162,000 - $198,000.

Equity
This role is eligible to participate in Cloudflare's equity plan.
Benefits
Cloudflare offers a complete package of benefits and programs to support you and your family. Our benefits programs can help you pay health care expenses, support caregiving, build capital for the future and make life a little easier and fun! The below is a description of our benefits for employees in the United States, and benefits may vary for employees based outside the U.S.
Health & Welfare Benefits

• Medical/Rx Insurance
• Dental Insurance
• Vision Insurance
• Flexible Spending Accounts
• Commuter Spending Accounts
• Fertility & Family Forming Benefits
• On-demand mental health support and Employee Assistance Program
• Global Travel Medical Insurance

Financial Benefits

• Short and Long Term Disability Insurance
• Life & Accident Insurance
• 401(k) Retirement Savings Plan
• Employee Stock Participation Plan

Time Off

• Flexible paid time off covering vacation and sick leave
• Leave programs, including parental, pregnancy health, medical, and bereavement leave