Interested in this AI/ML Engineer role at Proofpoint?
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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
Satori is Proofpoint's agentic AI business ,a startup operating inside a larger, established company. We build agents that automate the high\-volume, repetitive security work customers do by hand today, and we monetize that work through a consumption model. Our scope is the full agentic stack: Agent Foundry (no\-/low\-code building of agents), Agent Harness (the runtime that hosts and executes them), MCP Access (a governed, metered product that lets third\-party agents and systems reach Proofpoint data and APIs), the Consumption Ledger (the metering and monetization fabric underneath everything), and Mission Control (the customer\-facing plane where agents are managed and governed). The agents themselves — the missions span both Collaboration Security and Data Security.
Reporting to our new SVP of Product Marketing within our Threat Protection Organization (TGP), We are looking for a VP of Product Management to run Satori as a business.
This is a business\-owner role, not only a R\&D\-roadmap role.
Proofpoint expects its product leaders to own outcomes, not output. At Satori that bar is explicit: you will carry a direct, multi\-year revenue commitment that scales aggressively. You own the strategy, the product, the go\-to\-market, and the number.
This is a rare scope for a product leader and it asks for a true product leader, not a roadmap manager. Three things define it:
- You own the number, and you achieve it through influence. Most of the monetizable value will come from agents the BUs build on your platform which means your leverage is strategy, clear standards, and a platform good enough that BU teams choose to build on it. You will drive results across teams you don't manage.
- You own go\-to\-market. With a dedicated Product Marketing Manager as your partner, you'll shape the full motion: evangelism, Ideal Customer Profile definition, POC design and execution, customer training and education, field enablement, and pricing including quote and deal review. The viability of the business is yours to prove and to build on.
- You own a platform and a product line. The shared platform (Mission Control, Foundry, Harness, the Ledger, and the supervision/monetization standards) serves both BUs. On top of that, MCP Access is a product you ship and monetize directly.
Whatyou'llown
The Satori business. Revenue, the consumption/credits model, packaging, pricing, and the visibility into usage, value, price, and cost that lets us defend and expand it.
Product strategy for the Satori platform Mission Control, Agent Foundry, Agent Harness, MCP Access, and the Consumption Ledger including the shared standards (supervision patterns, monetization hooks, instrumentation, auditability, identity/access abstractions) that let BU teams build quality missions consistently, without a shadow BU forming.
MCP Access as a product line the governed, metered way third\-party agents and systems consume Proofpoint data and APIs.
Go\-to\-market, end to end evangelism, ICP, POCs, enablement, training, pricing, and quote review until Satori earns dedicated GTM investment.
The operating model in practice making the steering committee, the ring\-fenced BU mission teams, and charter\-based cross\-BU governance actually produce shipped value (see the Satori operating model).
People hiring and coaching the Satori PM(s) who report to you, and building an empowered product team that thinks in outcomes.
Whatyou'lldo
- Set and communicate a product strategy sharp enough that 1,350 engineers you don't manage choose to build on your platform.
- Run continuous discovery with customers, the field, and internal builders (BU R\&D, sales engineering, support, threat research) to find the shared problems worth solving.
- Run the GTM motion yourself: define who Satori is for, prove value in POCs, train customers and the field, and own pricing and deal economics.
- Operate the cross\-BU model steering committee, co\-signed reviews, decision log to resolve conflict quickly and keep mission teams ring\-fenced and moving.
- Build governance into the platform as a first\-class concern: guardrails, explainability, auditability, supervision pathways, and behavior\-change controls.
- Hire, coach, and hold the bar for your PM team; use AI fluently and work natively in a Git\-based, AI\-first product practice.
Whatwe'relooking for
- A product leader who has carried a business revenue, pricing, and GTM — not only a roadmap and a delivery org.
- Demonstrated ability to drive outcomes across teams you don't control, in ambiguous, fast\-moving, politically complex environments.
- Strong platform and product\-line experience: APIs, identity/access, monetization, or infrastructure — with the judgment to prioritize around outcomes over activity.
- Real GTM range: evangelism, ICP, POCs, enablement, packaging and pricing, and deal review.
- Credibility with engineers on architecture, APIs, auth, and telemetry tradeoffs and with commercial leaders on packaging, value, and GTM implications.
- A track record of building and coaching empowered product teams that own outcomes.
- Strong written and verbal communication, sound judgment, and a bias for action.
Nice to have
- Cybersecurity experience \-collaboration security, data security, identity, or security operations.
\- Usage\- or consumption\-based monetization models.
- Productizing third\-party integrations and ecosystem/partner offerings.
- 0\-to\-1 or startup\-style experience inside a larger company.
- Familiarity with agentic\-security and automation platform patterns, including competitors such as CrowdStrike Charlotte AI, Palo Alto Networks, Splunk SOAR, and Microsoft security automation.
What success looks like
- We hit the number and the consumption model is healthy enough to expand on.
- BU teams ship quality missions faster because the platform, standards, and operating model are clearer, more reusable, and better aligned to real needs.
- MCP Access is monetizing real third\-party consumption.
- Satori accelerates BU execution without taking over BU mission ownership.
- The Satori PM team is fully staffed against its open roles, capable, and empowered.
Failure modes to avoid
- Retreating to roadmap and delivery, and treating revenue, pricing, and GTM as someone else's job.
- Leading through authority instead of strategy and clarity and burning the cross\-BU trust the role depends on.
- Becoming a shadow mission\-PM team for the BUs, or a bottleneck to BU innovation.
- Letting governance, supervision, and behavior\-change controls become afterthoughts.
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!
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 in Demand for This Role
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 $214,900 based on 6,420 positions with disclosed compensation.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Proofpoint AI Hiring
Proofpoint has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Sunnyvale, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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