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Who is Forcepoint?
Forcepoint simplifies security for global businesses and governments. Forcepoint’s all-in-one, truly cloud-native platform makes it easy to adopt Zero Trust and prevent the theft or loss of sensitive data and intellectual property no matter where people are working. 20+ years in business. 2.7k employees. 150 countries. 11k+ customers. 300+ patents. If our mission excites you, you’re in the right place; we want you to bring your own energy to help us create a safer world. All we’re missing is you!
Forcepoint is seeking a Senior Product Manager to drive roadmap and execution in our Data Security Posture Management and AI Security product line. This role is the critical nexus between engineering, design, go-to-market, and customer-facing teams, driving the full product lifecycle from discovery through market adoption.
You’ll own part of the product direction and roadmap for Forcepoint’s DSPM and AI Security products. DSPM tools find sensitive data across an organization’s cloud and SaaS environments, assess its risk, and help fix problems. You’ll work with engineering, design, go-to-market, and customer-facing teams across the full product lifecycle, from early customer research to launch and adoption.
You’ll spend time with customers, including security architects and enterprise buyers. You’ll also work closely with engineers day to day, writing requirements, reviewing designs, and planning sprints. We’re looking for someone who can do both.
What you’ll do?
Shape strategy and roadmap
- Define and own your areas of the product vision and multi-quarter roadmap, working alongside other product managers.
- Track competitors and use what you learn to inform positioning and feature priorities.
- Use the jobs-to-be-done approach to turn customer needs into clear workflows with measurable security outcomes.
- Turn market trends, customer feedback, and regulatory requirements into specific product initiatives.
- Prioritize using product telemetry, win/loss analysis, and customer usage data.
Deliver the product
- Work with engineering on data discovery, classification, policy models, APIs, and remediation workflows.
- Write clear PRDs (product requirements documents), user stories, and acceptance criteria that engineers can build from with few open questions.
- Guide the discover, classify, assess, and remediate lifecycle across structured and unstructured data, SaaS applications, and cloud-native environments.
- Partner with UX/UI designers to make complex DSPM workflows simple and easy to use.
- Gather feedback from Sales, Solutions Engineering, and Customer Success and turn it into well-defined problems and prioritized requirements.
- Support go-to-market teams with competitive positioning, demo stories, and roadmap updates.
What you bring (core requirements):
- Product management experience in B2B or enterprise software, with growing ownership over time
- Experience with cybersecurity, data protection, or cloud infrastructure products
- A history of owning a product or major feature area from strategy through roadmap, delivery, and adoption
- Hands-on knowledge of at least one of these areas: Data security or DLP (data loss prevention) across endpoints, cloud, email, web, or networks DSPM, including data discovery, classification, risk assessment, and remediation in cloud data stores Cloud security platforms (CSPM, SSPM, CASB) with a focus on data or identity
Nice to have
- Experience in more than one of the areas above
- Not sure you meet every requirement? Apply anyway. We care about what you can do, and there are many routes into this role.
Don’t meet every single qualification? Studies show people are hesitant to apply if they don’t meet all requirements listed in a job posting. Forcepoint is focused on building an inclusive and diverse workplace – so if there is something slightly different about your previous experience, but it otherwise aligns and you’re excited about this role, we encourage you to apply. You could be a great candidate for this or other roles on our team.
The policy of Forcepoint is to provide equal employment opportunities to all applicants and employees without regard to race, color, creed, religion, sex, sexual orientation, gender identity, marital status, citizenship status, age, national origin, ancestry, disability, veteran status, or any other legally protected status and to affirmatively seek to advance the principles of equal employment opportunity.
Forcepoint is committed to being an Equal Opportunity Employer and offers opportunities to all job seekers, including job seekers with disabilities. If you are a qualified individual with a disability or a disabled veteran, you may request a reasonable accommodation if you are unable or limited in your ability to use or access the Company’s career webpage as a result of your disability. You may request reasonable accommodations by sending an email to [email protected]*.*
Applicants must have the right to work in the location to which you have applied.
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 9,079 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Forcepoint, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills in Demand for This Role
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,200 based on 947 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
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.
Forcepoint AI Hiring
Forcepoint has 2 open AI roles right now. They're hiring across AI Product Manager. Positions span Richmond, VA, US, Cork, County Cork, Ireland.
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 Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
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).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
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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