Senior Research Applied AI Engineer

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

The Role

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 at all. That’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.

What you'll do

• 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.

What we're looking for

• PhD in Computer Science or related field.
• 5+ years building software and ML systems, with recent hands-on work on LLM systems that went past the prototype stage, 7+ years preferred.
• 3+ years of research experience (understanding customer problems and deriving/brainstorming solutions)
• Experience with Claude Code, Cursor or similar, used daily, without lowering your standard for correctness and tests.
• Experience with third-party model APIs and open-weight models (we run on our own GPU fleet)
• Strong Python and solid ML fundamentals
• Familiarity with Kubernetes inference platform, Flyte for orchestration, LiteLLM / Langfuse and internal tooling for evaluation and tracing
• Experience building agents that do real work, and you’ve dealt with what goes wrong when they run for a long time
• Experience building evals that gave you a real answer, and you know how easily they can mislead
• Experience getting strong results out of frontier models, hosted or self-hosted

Nice to Have

• Experience fine-tuning or serving open-weight models
• Experience with automated prompt and pipeline optimization
• Experience with multi-GPU serving and or MCP and tool protocols
• Previous cybersecurity experience such as phishing or threat detection
• Open-source contributions or publications

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!

Consistent with Proofpoint values and applicable law, we provide the following information to promote pay transparency and equity. Our compensation reflects the cost of labor across several U.S. geographic markets, and we pay differently based on those defined markets as set out below. Pay within these ranges varies and depends on job-related knowledge, skills, and experience. The actual offer will be based on the individual candidate. The range provided may represent a candidate range and may not reflect the full range for an individual tenured employee. This role may be eligible for variable compensation and/or equity. We offer a competitive benefits package, including flexible time off, a comprehensive well-being program with two paid Wellbeing Days and two paid Volunteer Days per year, plus a three-week Work from Anywhere option.

Base Pay Ranges:

SF Bay Area, New York City Metro Area:

California (excludes SF Bay Area), Colorado, Connecticut, Illinois, Washington DC Metro, Maryland, Massachusetts, New Jersey, Texas, Washington, Virginia, and Alaska:

All other cities and states excluding those listed above: