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About This Role
Obsidian Security is the leading SaaS security platform, trusted by global enterprises like Snowflake, T\-Mobile, and Algolia. We protect 200\+ organizations across North America, Europe, the Middle East, Southeast Asia, Australia, and New Zealand, including many of the world's largest Fortune 1000 and Global 2000 companies.
Founded in 2017 and backed by top investors like Greylock, Obsidian was built to close a critical gap: securing SaaS apps where business happens—Microsoft 365, Salesforce, and hundreds more. The company does this by offering a complete SaaS security platform to reduce risk, detect and respond to threats, and prevent breaches at the source. Obsidian was built by leaders who redefined endpoint and identity security at CrowdStrike, Okta, Cylance, and Carbon Black. Now, they're transforming how SaaS is secured.
With AI driving rapid SaaS growth and complexity, agentic AI tools gain privileged access to sensitive data through integrations, creating new risks most security tools miss. Obsidian uniquely detects anomalous OAuth token activity and manages integration risks. Major announcements are on the horizon. Recognizing that SaaS security needs to evolve, Obsidian enables growing organizations to start with a lightweight, prevention\-focused browser extension and expand coverage over time.
With global momentum, a growing partner ecosystem including SentinelOne, Databricks, and Google Cloud, and a major fundraise ahead, Obsidian is scaling rapidly toward long\-term growth and IPO readiness.
Obsidian Security is looking for a Staff Product Manager, AI Governance \& Supply Chain Integration Risk to lead product strategy and execution for how customers understand, prioritize, and reduce risk across their SaaS and third\-party ecosystem.
We're looking for a PM who thinks beyond product features and focuses on the key outcomes customers care about most: reducing unknown third\-party risk, understanding where AI and agentic capabilities are enabled, prioritizing what matters, making risk explainable, and helping teams take action.
As SaaS vendors rapidly add AI and agentic capabilities into their applications, enterprises are struggling to answer basic but critical questions: Is AI enabled in our tenants? What does it do? What data can it access? Who can use it? Which agents, apps, or integrations can act on behalf of users? Is any of this governed consistently?
This role is not about managing a feature backlog. It is about identifying the most important customer and business problems, defining the outcomes we need to drive, and partnering with Engineering, Design, Product Marketing, Sales, Solutions Engineering, Customer Success, and leadership to deliver measurable customer value.
Responsibilities
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- Own the product strategy and roadmap for Supply Chain Risk \& AI Governance, with a clear focus on customer outcomes and business impact.
- Identify the most important customer problems related to SaaS supply chain risk, third\-party access, AI enablement, agentic capabilities, data exposure, and governance gaps.
- Define success in terms of outcomes such as risk reduction, prioritization quality, adoption, remediation progress, customer value, and revenue impact.
- Translate customer problems and desired outcomes into clear product requirements, roadmap priorities, and success metrics.
- Partner deeply with Engineering on solution direction, technical tradeoffs, sequencing, data models, integrations, risk scoring, and workflows.
- Work with Design to create experiences that make complex SaaS, identity, AI, agent, data access, and third\-party risk relationships understandable and actionable.
- Partner with Product Marketing, Sales, Solutions Engineering, Customer Success, and Support on positioning, launch readiness, field enablement, adoption, and feedback loops.
- Stay close to customer and market signals around SaaS AI adoption, AI governance, agentic workflows, third\-party risk, identity risk, and data exposure.
- Drive cross\-functional alignment, make tradeoffs visible, and push decisions forward in ambiguous situations.
- Own outcomes beyond launch by measuring whether the work is landing, learning from customer feedback, and adjusting based on results.
Required qualifications
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- 7\+ years of Product Management experience, ideally in B2B SaaS, cybersecurity, risk, compliance, data, platform, or enterprise software.
- Strong track record of thinking beyond features to define customer problems, desired outcomes, success metrics, and product strategies that drive measurable impact.
- Experience owning a product area from strategy through execution, launch, adoption, and iteration.
- Strong technical fluency, including comfort working with Engineering on APIs, data models, integrations, identity/access systems, event data, permissions, and enterprise architecture.
- Strong customer and business judgment, with the ability to prioritize high\-impact work and make clear tradeoffs.
- Ability to understand emerging customer problems in AI governance, SaaS risk, data access, and agentic workflows.
- Proven ability to lead cross\-functional teams without formal authority.
- Excellent written and verbal communication skills.
Salary Context
This $184K-$223K range is above the median for AI Product Manager roles in our dataset (median: $187K across 164 roles with salary data).
View full AI Product Manager salary data →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 4,133 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Obsidian Security, 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 Required
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 $213,800 based on 610 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($203K) sits 5% below the category median. Disclosed range: $184K to $223K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Obsidian Security AI Hiring
Obsidian Security has 1 open AI role right now. They're hiring across AI Product Manager. Based in New York, NY, US. Compensation range: $223K - $223K.
Location Context
AI roles in New York pay a median of $211,000 across 2,760 tracked positions. That's 5% above the national 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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.
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