About this role
Cloudflare needs an experienced technical leader to solve the challenge of identifying automated, fraudulent, and malicious activity across an intelligent global network that handles trillions of requests per month. Operating within the Detection department, this principal team member will design advanced detection systems and machine learning models that protect customers from evolving internet threats while navigating the complexities of noisy or delayed ground truth data.
The person stepping into this role will own the detection measurement framework, lead research and design for fraud and bot mitigation, and collaborate closely with machine learning engineers, data engineers, and product teams. Ideal candidates combine strong applied statistics and AI methodology with the ability to make high-stakes technical decisions when information is incomplete, showing comfort with ambiguity and adversaries that actively fight back.
What you will do
- Lead the research, design, and evaluation of detection models that identify automated, fraudulent, and malicious activity across internet-scale data.
- Mentor other team members on uncovering patterns and behaviors that distinguish adversaries from legitimate users.
- Own the detection measurement framework by defining metrics and evaluation strategies for problems where ground truth is noisy, delayed, or contested.
- Drive the adoption of promising AI and machine learning techniques while representing Cloudflare in the broader research community.
- Build cross-functional partnerships with machine learning engineers, data engineers, and product teams to influence roadmaps and align detection strategy with company priorities.
- Shape long-term detection strategy by identifying gaps and opportunities spanning multiple product areas.
What you need
- Bring at least 8 to 10 years of professional experience working in data science, machine learning engineering, or software engineering.
- Demonstrate hands-on experience across fraud, abuse, and bot detection on large, high-velocity traffic.
- Possess solid applied statistics, machine learning, and AI methodology fundamentals with fluency in large-scale data.
- Show a demonstrated ability to make ambiguous, high-stakes technical decisions with incomplete information.
- Maintain proficiency with Python and SQL in production environments.
Nice to have
- Experience building detections when ground truth is scarce or labels are weak, delayed, or absent
- Understanding of how to act on detections without tipping your hand or eroding future visibility
- Pragmatic approach to complexity that favors simple solutions over disproportionately costly gains
- Strong programming and engineering best practices applied in both research and production code
- Ability to connect work to business impact and communicate clearly across technical and non-technical stakeholders
Benefits
- Medical, dental, and vision insurance
- Flexible spending accounts and commuter spending accounts
- Fertility and family forming benefits, alongside on-demand mental health support and employee assistance programs
- Short and long-term disability insurance, life and accident insurance, 401(k) retirement savings plan, and employee stock participation plan
- Flexible paid time off covering vacation and sick leave
- Leave programs including parental, pregnancy health, medical, and bereavement leave
Skills and tech stack
Questions candidates ask
What locations are available for this role?
This hybrid position has available locations in New York and London.
What is the expected compensation for this position?
For New York City based hires, the estimated annual salary ranges from $215,000 to $270,000, and the role is eligible to participate in Cloudflare's equity plan.
What kind of technical environment will I be working in?
You will work with large-scale data, utilizing Python and SQL in production environments to build advanced detection systems and machine learning models.
What departments will I collaborate with?
You will drive cross-functional partnerships with machine learning engineers, data engineers, and product teams to align detection strategy with company priorities.