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Lab Summary:
Are you passionate about transforming cutting-edge technologies into services that impact millions in our daily lives? At the Mobile Platform Solutions (MPS) Enterprise Security & Innovation Lab (ESIL), we are looking for passionate product managers to share their creative vision and build innovative security and AI offerings for Samsung's portfolio of mobile devices and solutions. In this role, you will join a collaborative team of world-class designers, researchers, and engineers specializing in security, privacy, AI/ML, and mobile services across consumer and enterprise segments.
Come join the Samsung MPS ESIL team and be a leader in bringing futuristic services to life!
Position Summary:
The Senior Product Manager will work closely with internal teams, including business development, engineering, and design, to define requirements and roadmaps. This is a unique opportunity to lead one or more of our advanced mobile security and AI offerings. The ideal candidate will own and drive product roadmaps, strategy, and execution of intelligent mobile security and productivity solutions for consumers and businesses.
Position Responsibilities:
- Drive and execute the product vision, strategy, and roadmap for advancing Samsung mobile security and AI offerings
- Leverage expertise in Android and Android Enterprise security to identify opportunities for Samsung to innovate and deliver enhanced Security and AI capabilities to Samsung's mobile customers
- Collaborate with industry-leading ecosystem partners, including mobile OS, device management, and endpoint protection platforms, as well as ISVs, system integrators, and managed service providers, to increase market share
- Analyze market trends, customer needs, and the competitive landscape to identify opportunities across consumer and enterprise segments
- Engage with customers and partners to gather feedback and validate product direction
- Collaborate with engineering, sales, and marketing teams to ensure robust GTM plans and successful product launches
- Serve as a Subject Matter Expert for both internal and external stakeholders
- Demonstrate customer obsession by championing user experience with deep customer empathy
Required Skills:
- Engineering degree in EE, CS (Master's or MBA preferred) or equivalent combination of education, training, and experience
- 7+ years of product management experience in mobile security, device management, mobile threat defense, or related fields
- Understanding of Android OS security architecture, including security framework APIs, permission model, secure boot, and trusted execution environments
- Understanding of device-level threat models and protections; familiarity with MDM/UEM platforms and mobile threat defense (MTD) solutions
- Familiarity with Android Enterprise APIs, capabilities, and deployment scenarios
- Proven track record of successfully driving innovation and launching mobile solutions
- Strong vision for the future of AI-powered device security and productivity solutions
- Experience working with cross-functional teams in a fast-paced environment
- Excellent communication, presentation, and stakeholder management skills
- Ability to build excellent user experience and lead/present at team and executive levels
- Willingness to travel internationally (<25%)
Special Attributes:
- Background in B2C mobile security product management
- Knowledge of AI/ML and how they might be applied to mobile security, threat detection, and productivity
- Familiarity with cloud-based security platforms (e.g., XDR, SIEM, SOAR) as they relate to mobile device security
Additional Information
Disclosure of Trade Secrets
Samsung has a strict policy on trade secrets. In applying to Samsung and progressing through the recruitment process, you must not disclose any trade secrets of a current or previous employer.
Essential Job Functions
This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, and frequently operate standard office equipment, such as telephones and computers.
Samsung Research America is committed to complying with all Federal, State and local laws related to the employment of qualified individuals with disabilities. If you are an individual with a disability and would like to request a reasonable accommodation as part of the employment selection process, please contact the recruiter or email [email protected].
Equal Employment Opportunity
At Samsung, we believe that innovation and growth are driven by an inclusive culture and a diverse workforce. We aim to create a global team where everyone belongs and has equal opportunities, inspiring our talent to be their true selves. Together, we are building a better tomorrow for our customers, partners, and communities.
Samsung Research America is committed to employing a diverse workforce, and provide Equal Employment Opportunity for all individuals regardless of race, color, religion, gender, age, national origin, marital status, sexual orientation, gender identity, status as a protected veteran, genetic information, status as a qualified individual with a disability, or any other characteristic protected by law.
For more information regarding protection from discrimination under Federal law for applicants and employees, please refer to this link: Pay Transparency
Salary Context
This $163K-$224K range is above the median for AI Product Manager roles in our dataset (median: $193K across 93 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 8,943 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Samsung Research America, 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 862 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($194K) sits 12% below the category median. Disclosed range: $163K to $224K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Samsung Research America AI Hiring
Samsung Research America has 1 open AI role right now. They're hiring across AI Product Manager. Based in Mountain View, CA, US. Compensation range: $224K - $224K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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