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 RoleAI 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/Staff 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 doAnswer 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 behaviour 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.
5+ years building software and ML systems, with recent hands-on work on LLM systems that went past the prototype stage.
Experience building agents that do real work, and you’ve dealt with what goes wrong when they run for a long time.
Experience with third-party model APIs and open-weight models (we run on our own GPU fleet)
Experience with Kubernetes inference platform, Flyte for orchestration, LiteLLM / Langfuse and internal tooling for evaluation and tracing.
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.
Strong Python and solid ML fundamentals.
Experience with Claude Code, Cursor or similar, used daily, without lowering your standard for correctness and tests.
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:
Base Pay Range: 200,300.00 - 293,810.00 USDCalifornia (excludes SF Bay Area), Colorado, Connecticut, Illinois, Washington DC Metro, Maryland, Massachusetts, New Jersey, Texas, Washington, Virginia, and Alaska:
Base Pay Range: 167,300.00 - 245,355.00 USDAll other cities and states excluding those listed above:
Base Pay Range: 152,900.00 - 224,235.00 USDSkills Required
- 5+ years building software and machine learning systems
- Recent hands-on experience building LLM systems beyond the prototype stage
- Experience building agents that perform real work and handling long-running agent failures
- Experience with third-party model APIs and open-weight models
- Experience with Kubernetes inference platforms
- Experience with Flyte orchestration
- Experience with LiteLLM, Langfuse, and internal evaluation and tracing tools
- Experience building reliable evaluation systems and understanding evaluation limitations
- Experience achieving strong results with hosted or self-hosted frontier models
- Strong Python skills and solid machine learning fundamentals
- Daily experience using Claude Code, Cursor, or similar coding agents while maintaining correctness and testing standards
- Experience fine-tuning or serving open-weight models
- Experience with automated prompt and pipeline optimization
- Experience with multi-GPU serving, MCP, or tool protocols
- Previous cybersecurity experience, such as phishing or threat detection
- Open-source contributions or publications
Proofpoint Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Proofpoint and has not been reviewed or approved by Proofpoint.
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Healthcare Strength — Healthcare coverage spans medical, dental, vision, life, and disability, complemented by global physical, mental, and financial health programs. Wellbeing resources such as mindfulness, resilience, and meditation courses are explicitly highlighted.
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Leave & Time Off Breadth — Time-off provisions include PTO, paid holidays and sick days, with parental and family medical leave available. Added flexibility appears in wellness days, a hybrid-first model, and limited work-from-anywhere periods.
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Fair & Transparent Compensation — Pay is considered competitive in many roles and settings, with external recognition indicating strong standing relative to peers. Feedback suggests employees in several departments view compensation favorably when considering base, bonus, and benefits together.
Proofpoint Insights
What We Do
We provide the most effective cybersecurity and compliance solutions to protect people on every channel including email, the web, the cloud, and social media.
