Senior Applied AI Engineer

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.

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

AI is moving faster than any single product team can keep up with. For a security company, the hard question isn’t “can a model do this?” It’s which approach actually works, what it costs, how fast and reliable it is, and whether it’s worth building t’s what this team answers. We’re an Applied AI Research team, and research is our product.

We take important questions the company can’t yet answer and turn them into evidence people can act on. As a Senior Applied AI Engineer, you’ll own a line of research from start to finish: frame the question, build what’s needed to answer it, measure honestly, and reach a decision. When something is ready to move to another team, you’ll help them deploy it well, advising on the AI side and on how to monitor it once it’s live.

Sometimes the answer is “this works, here’s the proof.” Sometimes it’s “this won’t work, and we found out in three weeks instead of six months.” Both count as success here.

• Answer hard questions with evidence.
• Take a question nobody has answered yet.
• Make it testable, set a baseline, run the smallest experiment that settles it, and come back with an answer the company can act on.
• Build systems that improve themselves.
• A lot of our agentic work runs in long loops: run an evaluation, study the results, produce a better version, run it again.
• You’ll build systems that improve on their own and recover when they go wrong.
• Make agent behavior measurable.
• Extend our evaluation platform so we can tell whether an agent is genuinely good - task suites, regression checks, and honest tradeoffs between quality, cost and speed.
• It’s something we build and maintain for the whole company.
• Work across hosted and self-hosted models. Most of what we do runs on third‑party model APIs. We also serve open‑weight models on our own GPUs and are pushing further into fine‑tuning and efficient serving.
• You’ll help us work out which jobs suit which.
• Improve how we build with coding agents. We use coding agents every day and benchmark them against our own codebases.
• You’ll help our engineers get more out of them.
• Start from real problems.
• Spend time with stakeholders, understand their problems rather than collecting feature requests, and turn what you hear into research worth doing.
• Put working things in people’s hands.
• Build prototypes, playgrounds and APIs so stakeholders can try an idea themselves and learn alongside us.
• Often that’s how the decision gets made.
• Help get it into production.
• When something works, we don’t just hand it over.
• You’ll advise the team deploying it - on the AI side, and on how to monitor and understand the system once it’s running.
• Help the team get better.
• Mentor engineers, share what you learn, and help the team ask better questions.

• Bachelors, Masters or PhD in Computer Science or related field or equivalent experience
• 5+…