Senior Applied AI Engineer

Proofpoint

. Frame unanswered questions, make them testable, establish baselines and run focused experiments .

Posted 9/15/2026full-timeColorado • United StatesSenior💰 $167,300 - $245,355 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and maintaining machine learning systems, particularly with large language models, while effectively engaging stakeholders and mentoring engineering teams. Proficient in evaluating tradeoffs in quality, cost, and speed, and optimizing engineering workflows through innovative solutions.

Highest-signal resume keywords

Machine Learning Systems DevelopmentLarge Language Model ExperiencePython ProgrammingKubernetes Inference Platform FamiliarityThird-Party Model API Integration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software DevelopmentMachine Learning FundamentalsAgent-Based SystemsEvaluation BuildingAutomated Prompt OptimizationMulti-GPU ServingOpen-Weight Model Fine-TuningLong-Running Agent Failure HandlingRegression ChecksBenchmarking

Soft Skills

Stakeholder EngagementMentoringProblem-Solving

Tools & Technologies

Claude CodeCursorKubernetesFlyteLiteLLMLangfuseInternal Tooling

Industry Keywords

CybersecurityPhishing DetectionThreat DetectionOpen-Source Contributions

Tech Stack

Tools & technologies

Cyber SecurityKubernetesPython

About the role

Key responsibilities & impact

Frame unanswered questions, make them testable, establish baselines and run focused experiments
Build self-improving agentic systems that recover from failures
Extend and maintain the company-wide evaluation platform, including task suites and regression checks
Evaluate tradeoffs among quality, cost and speed
Work with third-party model APIs and open-weight models on company GPUs
Improve engineering workflows using coding agents and benchmark them against internal codebases
Engage stakeholders to identify real problems and turn them into research opportunities
Build prototypes, playgrounds and APIs for stakeholder experimentation
Advise deployment teams on AI implementation and production monitoring
Mentor engineers and share research learnings

Requirements

What you’ll need

5+ years building software and ML systems; 7+ years preferred
Recent hands-on work on LLM systems that went past the prototype stage
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
Strong Python and solid ML fundamentals
Familiarity with Kubernetes inference platform
Familiarity with Flyte for orchestration
Familiarity with LiteLLM, Langfuse and internal tooling for evaluation and tracing
Experience building agents that do real work and handling long-running agent failures
Experience building evaluations and understanding how they can mislead
Experience getting strong results from frontier models, hosted or self-hosted
Preferred: experience fine-tuning or serving open-weight models
Preferred: experience with automated prompt and pipeline optimization
Preferred: experience with multi-GPU serving and/or MCP and tool protocols
Preferred: previous cybersecurity experience such as phishing or threat detection
Preferred: open-source contributions or publications

Benefits

Comp & perks

Competitive compensation
Comprehensive benefits
Flexible work environment
Annual wellness and community outreach days
Always on recognition for your contributions
Global collaboration and networking opportunities
Flexible time off
Comprehensive well-being program
Two paid Wellbeing Days per year
Two paid Volunteer Days per year
Three-week Work from Anywhere option
Variable compensation and/or equity may be offered