Senior Applied AI Engineer

Proofpoint

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
North CarolinaColorado
Salary
$200,300–$293,810 / yr
Posted
9 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $204k
This role $247k
$146k most similar roles pay here $310k

This role pays more than 79% of similar roles. Most pay $162,000–$246,150 — the shaded band above. At the midpoint, this role pays about $247k versus about $204k for comparable roles.

Based on 240 similar postings.

Employer

About Proofpoint

Proofpoint is a cybersecurity company specializing in email security, threat protection, data loss prevention, and compliance. Privately held by Thoma Bravo since 2021, it serves over 500,000 organizations including 87 of the Fortune 100.

Proofpoint currently has 15 open roles on FindRole.

Listed pay typically runs $136,200–$214,005 across 9 roles with salary data.

Most-posted roles

View all roles at Proofpoint

At a glance

TL;DR · Senior Applied AI Engineer

As a Senior Applied AI Engineer on the Applied AI Research team, you will own research initiatives from inception to completion by framing questions, building necessary systems, and providing evidence-based decisions for the company. You will develop self-improving systems, build evaluation platforms to measure agent behavior regarding quality, cost, and speed, and manage both hosted third-party model APIs and open-weight models on internal GPU fleets. Your daily work involves creating prototypes, advising teams on production deployment, and improving coding agent workflows. The role requires expertise in Python, ML fundamentals, Kubernetes inference platforms, Flyte for orchestration, LiteLLM, Langfuse, and tools like Claude Code or Cursor. You will solve complex problems regarding the reliability and cost-effectiveness of AI models within a cybersecurity context to determine which technologies are viable for production use.

What you'll do

  • Conduct research to turn complex questions into actionable evidence for the company.
  • Build self-improving systems and agentic workflows that can recover from errors automatically.
  • Develop and maintain evaluation platforms to measure agent performance, cost, and speed.
  • Manage both third-party model APIs and self-hosted open-weight models on internal GPU fleets.
  • Improve engineering productivity by benchmarking and optimizing the use of coding agents.
  • Build prototypes, playgrounds, and APIs for stakeholders to test new AI ideas.
  • Advise product teams on deploying, monitoring, and maintaining AI systems in production.
  • Mentor engineers and share technical knowledge to improve overall team capabilities.

What we're looking for

  • Must have 5+ years of experience building software and ML systems.
  • Must have recent hands-on experience with LLM systems that progressed beyond the prototype stage.
  • Must have experience building agents for long-running tasks and managing associated failures.
  • Must have experience working with both third-party model APIs and open-weight models on GPU fleets.
  • Must have experience with Kubernetes inference platforms, Flyte orchestration, LiteLLM, and Langfuse.
  • Must have experience building reliable evaluation systems to measure agent performance.
  • Must possess strong Python programming skills and solid machine learning fundamentals.
  • Must be proficient with tools like Claude Code or Cursor for daily development tasks.

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