Senior Applied AI Engineer

About the role

Proofpoint is a global leader in human- and agent-centric cybersecurity, protecting 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. The mission is to safeguard the digital world and empower people to work securely and confidently. The Applied AI Research team answers hard questions about AI approaches, building evidence for decisions, building self‑improving systems, extending evaluation platforms, working with hosted and self‑hosted models, improving coding agents, researching from real problems, building prototypes, and helping deploy successful work. The team operates guided by Proofpoint’s 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.

Responsibilities

• Answer hard questions with evidence: make questions testable, set baselines, run the smallest experiment that settles it, and return actionable answers.
• Build systems that improve themselves: run evaluations, study results, produce better versions, recover from failures.
• Make agent behavior measurable: extend the evaluation platform with task suites, regression checks, and honest tradeoffs between quality, cost, and speed.
• Work across hosted and self‑hosted models: use third‑party model APIs, serve open‑weight models on GPUs, and engage in fine‑tuning and efficient serving.
• Improve how we build with coding agents: benchmark coding agents against internal codebases and help engineers get more out of them.
• Start from real problems: spend time with stakeholders, understand their issues, and turn those insights into research worth doing.
• Put working things in people’s hands: build prototypes, playgrounds, and APIs so stakeholders can try ideas and learn alongside the team.
• Help get it into production: advise deploying teams on the AI side and on monitoring and understanding the system once live.
• Help the team get better: mentor engineers, share learnings, and help the team ask better questions.

Requirements

• 5+ years building software and ML systems, with recent hands‑on work on LLM systems that went past the prototype stage (7+ years preferred).
• Experience with Claude Code, Cursor or similar, used daily without lowering standards for correctness and tests.
• Experience with third‑party model APIs and open‑weight models (run on own GPU fleet).