Interested in this AI/ML Engineer role at Proofpoint?
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About This Role
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
About the Role
We're looking for an AI Engineer II to design and ship internal AI-powered tools end-to-end. You'll own AI-powered applications, workflow automations, and integrations from concept through production, evaluating and integrating new AI capabilities as you go. This is a hands-on building role: you'll write production code, not just prototype it, while partnering with business stakeholders and growing into fuller ownership at the next level.
Responsibilities
- Design and build internal AI-powered tools and web applications end-to-end, including front-end, back-end, data, API, and LLM/AI integrations.
- Own AI applications, workflow automations, and systems integrations from requirements gathering and technical design through production deployment and iteration.
- Partner with Sales Operations, Revenue Analytics, and cross-functional stakeholders to translate business problems into scalable technical solutions that deliver measurable value.
- Evaluate and integrate new AI models, APIs, frameworks, and development approaches based on business needs, technical feasibility, and performance.
- Develop production-quality applications with appropriate testing, documentation, monitoring, and maintainability; troubleshoot and improve solutions post-launch based on user feedback and performance.
- Build reusable AI capabilities, integrations, documentation, and development patterns that can be leveraged across teams and use cases.
- Communicate technical concepts, solution designs, tradeoffs, and outcomes to technical and non-technical stakeholders, including presenting work to cross-functional audiences.
Qualifications
Required
- 1–2 years of hands-on experience building software, data products, or AI-powered tools through professional, internship, academic, or substantial independent project experience.
- Strong proficiency in Python and SQL, with experience in data analysis, application development, and statistical or machine-learning workflows.
- Hands-on experience with machine learning and statistical modeling, including regression, classification, feature engineering, and model evaluation.
- Experience working with large structured datasets using Snowflake, relational databases, or comparable cloud data platforms.
- Experience developing or integrating REST APIs and backend services using FastAPI or similar frameworks, along with familiarity with modern web application development.
- Demonstrated ability to ship functional tools or applications to real users and evaluate their performance using appropriate technical or quantitative measures.
- Strong problem-solving, communication, and collaboration skills, including the ability to learn unfamiliar technologies, incorporate stakeholder feedback, and explain technical tradeoffs to non-technical audiences.
Preferred
- Experience developing applications using LLMs, RAG, embeddings/vector search, prompt engineering, and LLM APIs such as OpenAI or Anthropic.
- Familiarity with AI agents, tool/function calling, intent routing, retrieval systems, and workflow orchestration.
- Experience with React and TypeScript or another modern front-end framework.
- Experience with Docker, AWS, or comparable cloud and deployment technologies.
- Familiarity with ETL/ELT pipelines, data modeling, JSON/metadata mappings, and multi-system data integrations.
- Experience with Snowflake, Salesforce, or other GTM/revenue data systems.
Logistics
- Full-time
- Reports to Director, Revenue Analytics
- Onsite/hybrid/remote per team policy
#LI-JK1
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: 111,300.00 - 174,900.00 USD California (excludes SF Bay Area), Colorado, Connecticut, Illinois, Washington DC Metro, Maryland, Massachusetts, New Jersey, Texas, Washington, Virginia, and Alaska:
Base Pay Range: 93,300.00 - 146,630.00 USD All other cities and states excluding those listed above:
Base Pay Range: 83,600.00 - 131,340.00 USD
Salary Context
This $83K-$174K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Proofpoint, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($129K) sits 39% below the category median. Disclosed range: $83K to $174K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Proofpoint AI Hiring
Proofpoint has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span TX, US, Chicago, IL. Compensation range: $174K - $174K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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