About Us:
Proofpoint is a global leader in human- and agent-centric cybersecurity. We protect how people, data, and AI agents connect across email, cloud, and collaboration tools. Over 80 of the Fortune 100, 10,000 large enterprises, and millions of smaller organizations trust Proofpoint to stop threats, prevent data loss, and build resilience across their people and AI workflows. Our mission is simple: safeguard the digital world and empower people to work securely and confidently. Join us in our pursuit to defend data and protect people.
How We Work:
At Proofpoint you’ll be part of a global team that breaks barriers to redefine cybersecurity guided by our BRAVE core values:
Bold in how we dream and innovate
Responsive to feedback, challenges and opportunities
Accountable for results and best in class outcomes
Visionary in future focused problem-solving
Exceptional in execution and impact
We are seeking a Machine Learning Engineer with at least 3-4 years of software engineering and ML systems experience to join our collaborative Machine Learning Engineering Team at Proofpoint. This role is integral to designing, building, and scaling production ML systems for cybersecurity challenges. You will take ownership of the full ML lifecycle—from data pipelines and feature engineering to model training, serving, and monitoring at scale. You will work closely with data scientists, data engineers, and security teams to build robust, performant systems that detect and prevent cyber threats in real time.
As a core team member, you will play a crucial role in architecting scalable ML systems and ensuring production excellence. You will own the end-to-end development and deployment of ML models, from infrastructure design to optimization and monitoring. You work closely with data scientists, MLOps engineers, data engineers, and security specialists to ensure models scale, perform reliably, and integrate seamlessly into our production environments.
We are looking for someone with a strong software engineering foundation—solid CS fundamentals, system design experience, and a proven track record building production systems. You should have hands-on experience with ML frameworks (PyTorch, TensorFlow, Scikit-learn) and the engineering discipline to write clean, testable, maintainable code. Experience with ML deployment platforms, model serving, feature stores, and monitoring systems is essential. Familiarity with adversarial ML, cybersecurity challenges, and NLP is a plus, but we prioritize strong engineering fundamentals and the ability to learn new ML domains quickly.
Your contributions will drive our ability to build reliable, scalable systems that detect cyber threats at scale, reinforcing Proofpoint's mission to protect organizations from security risks.
- Design and build scalable ML pipelines and data infrastructure for high-throughput threat detection systems.
- Own the end-to-end deployment and monitoring of ML models in production, including A/B testing and performance optimization.
- Architect model serving solutions (REST APIs, batch prediction, streaming) that meet latency and throughput requirements.
- Build feature pipelines and data infrastructure to support model training and inference at scale.
- Collaborate with data scientists to understand model requirements and translate them into production systems.
- Implement monitoring, logging, and alerting for ML models in production to detect performance degradation and failures.
- Optimize model performance and resource utilization, managing compute costs and inference latency tradeoffs.
- Actively engage with the team including data scientists, engineers, ML platform specialists, security analysts, and product management to deliver robust solutions.
- Strong software engineering fundamentals: proficiency in Python, solid understanding of data structures, algorithms, and system design.
- Experience building ML systems end-to-end: data pipelines, feature engineering, model training, model serving, and monitoring.
- Hands-on experience with ML frameworks: PyTorch, TensorFlow, or Scikit-learn in production settings.
- Familiarity with ML deployment and serving frameworks: MLflow, TensorFlow Serving, KServe, or similar.
- Familiarity with goLang
- Experience with cloud platforms, particularly AWS (EC2, S3, SageMaker, Lambda, RDS) for building scalable systems.
- Understanding of software engineering best practices: testing, CI/CD, code review, and version control.
- Basic understanding of adversarial ML concepts and security analytics is a plus.
Why Proofpoint?
At Proofpoint, we believe that an exceptional career experience includes a comprehensive compensation and benefits package. Here are just a few reasons you’ll love working with us:
Competitive compensation
Comprehensive benefits
Career success on your terms
Flexible work environment
Annual wellness and community outreach days
Always on recognition for your contributions
Global collaboration and networking opportunities
Our Culture:
Our culture is rooted in values that inspire belonging, empower purpose and drive success-every day, for everyone.
We encourage applications from individuals of all backgrounds, experiences, and perspectives. If you need accommodation during the application or interview process, please reach out to [email protected].
How to Apply
Interested? Submit your application along with any supporting information- we can’t wait to hear from you!
