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About This Role
At Braze, we have found our people. We're a genuinely approachable, exceptionally kind, and intensely passionate crew.
We seek to ignite that passion by setting high standards, championing teamwork, and creating work\-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization.
To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success.
Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one\-of\-a\-kind vibrancy into our culture.
If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can't wait to meet you.
#### The Role
Braze produces a lot of content — marketing campaigns, blog posts, customer stories, courses, lessons, documentation — and most of it is still written, reviewed, and published the way it was five years ago. AI changes what's possible here, but only if someone owns the work of figuring out which parts of the content workflow actually benefit from automation, building the tools, and getting them into people's daily hands. That's this role. You'll own the AI content operations product roadmap inside Content Platforms: identifying where content creators spend time on work that doesn't add proportional value, designing tools that remove the friction, shipping those tools into the Sanity CMS and the broader content stack, and driving adoption with the marketing and learning teams that use them.Teams across Braze are just starting to figure out how AI fits into their day\-to\-day work. There is no settled playbook, and the people closest to the business problems — the writers, designers, instructional designers, marketers, and operators making content every day — are the ones discovering what's possible. Your job is to learn from them first. The good ideas will not come out of an abstract roadmap exercise; they will come out of sitting next to people doing the work, watching where they get stuck, and noticing the small AI\-powered moves they're already making on their own.This is not a research role and it's not a presentations role. You'll write code, ship prototypes, pair with engineers, and measure adoption. AI\-assisted development is how we work — you'll be expected to use it, build with it, and be opinionated about it.
You'll sit on the Content Platforms team as a senior individual contributor. No direct reports, but a strong remit to lead — setting direction, raising the bar, and pulling colleagues across teams toward better solutions.
#### Room To Grow
The role itself is Senior TPM, Content Platforms — the parenthetical reflects initial focus, not permanent scope. The team owns a broader technical content operations remit, and as this work matures we expect the role to expand into adjacent problem areas. The right candidate sees AI content operations as a way in, not the whole job.
#### Why This Role Matters For You
The job most product managers say they want — owning a real product, shipping it, watching adoption climb — is hard to find inside a large company. This role is one of them. Content Platforms is a small, fast\-moving team. The marketing and learning organizations are real customers with real pain points. The tools you ship will land in people's daily workflow within weeks of shipping, not quarters.
You'll also be in a rare seat in terms of timing. The content operations function is in the middle of a hard reset around AI, and most of the people deciding how that plays out at enterprise scale are doing it badly. This role puts you in a position to shape how it gets done at Braze — and to develop the judgment that will be in short supply across the industry for a long time.
This is a role for people who'd rather build the tools than write a deck about them.
#### What You'll Do
- Embed with marketing and learning teams to deeply understand how content actually gets made — research, drafting, review, publishing, optimization — and where the friction lives. Pay particular attention to the AI experiments people are already running on their own; those are usually where the real opportunities surface
- Own the AI content operations product roadmap: what we build, what we buy, what we kill
- Ship AI\-powered tools that meaningfully shorten the path from idea to published content: metadata generation, doc\-to\-CMS conversion, content review and quality checks, research assistants, asset generation, and more we haven't named yet
- Build functional prototypes independently to validate ideas and sharpen requirements before full engineering investment
- Drive adoption: a tool that doesn't change someone's daily workflow doesn't count
- Make smart build\-vs\-buy decisions — knowing when to write internal tooling and when an existing or emerging vendor is the better answer
- Partner with the Sanity platform investment, since most of these tools surface inside the CMS or against its content
#### What We're Looking For
PMs are encouraged to apply, but so are solutions architects, solutions consultants, engineers, and others with a background in content management and content operations. Read the rest as a description of the work, not a checklist of prior titles.
#### Content Operations Background
- Familiar with how modern content teams actually work — content calendars, brief\-to\-publish workflows, review and approval, SEO, localization touchpoints, asset management
- Has opinions about where AI usefully fits in those workflows today, and where it doesn't yet — and is comfortable holding those opinions loosely as the people closer to the work teach you what you're missing
- Curious about content as a system, not just a deliverable — what we publish, how it gets discovered, how it gets reused, how it shapes the brand
#### Technical Foundation
- Deeply familiar with headless CMS architecture; Sanity experience is a strong plus
- Comfortable with Node.js, TypeScript, and modern web frameworks
- Able to build simple web apps and functional prototypes on your own — you won't be shipping complex systems to production solo, but you can independently validate an idea end\-to\-end
- Comfortable building with AI development tools like Claude Code or Cursor, and with the model APIs underneath them
- Has opinions about evaluating, prompting, and integrating LLMs into real workflows — not just experimenting in a chat window
#### Product Instincts
- Comfortable architecting tools that connect multiple systems (CMS, analytics, model providers, internal services) and reasoning about long\-term design tradeoffs
- Truly embedded with engineering — not just mediating through tickets and grooming sessions, but collaborating in real time, pairing on problems, and helping the team move faster using the AI development tools available today
- Can hold your own in technical conversations without needing to defer every decision
- Experienced shipping internal tools, where the bar for adoption is higher than external products and "we built it for you" is not a valid pitch
#### Mindset
- Iterative by nature: you'd rather ship a rough thing into a real workflow and learn than over\-plan before moving
- Genuinely curious about what AI makes possible — not as a buzzword, but as a tool you use and build with
- Systems thinker: you design for the problems you haven't seen yet, not just the ones in front of you
For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $132,000 and $209,000/year, with an expected On Target Earnings (OTE) between $147,000 and $232,000/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.
WHAT WE OFFER
*Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country* *here**. More details on benefits plans will be provided if you receive an offer of employment.*
From offering comprehensive benefits to fostering hybrid ways of working, we've got you covered so you can prioritize work\-life harmony. Braze offers benefits such as:
- Competitive compensation that may include equity
- Retirement and Employee Stock Purchase Plans
- Flexible paid time off
- Comprehensive benefit plans covering medical, dental, vision, life, and disability
- Family services that include fertility benefits and equal paid parental leave
- Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend
- A curated in\-office employee experience, designed to foster community, team connections, and innovation
- Opportunities to give back to your community, including an annual company\-wide Volunteer Week and donation matching
- Employee Resource Groups that provide supportive communities within Braze
- Collaborative, transparent, and fun culture recognized as a Great Place to Work®
ABOUT BRAZE
Braze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging™. Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses. Built on a foundation of composable intelligence, BrazeAI™ allows marketers to combine and activate AI agents, models, and features at every touchpoint throughout the Braze Customer Engagement Platform for smarter, faster, and more meaningful customer engagement. From cross\-channel messaging and journey orchestration to Al\-powered decisioning and optimization, Braze enables companies to turn action into interaction through autonomous, 1:1 personalized experiences.
The company has been consistently recognized as a Leader in marketing technology by industry analysts, and was named a G2 "Best of Marketing and Digital Advertising Software Product" in 2026\. Braze was also named a 2026 Best Places to Work by Built In, a 2025 America's Greenest Companies by Newsweek, and a 2025 Fortune Best Workplace in Technology™ by Great Place To Work®. Braze is also proudly certified as a Great Place to Work® in the U.S., the UK, Australia, and Singapore.
The company is headquartered in New York with offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, São Paulo, Singapore, Seoul, Sydney and Tokyo.
BRAZE IS AN EQUAL OPPORTUNITY EMPLOYER
At Braze, we strive to create equitable growth and opportunities inside and outside the organization.
Building meaningful connections is at the heart of everything we do, and that includes our recruiting practices. We're committed to offering all candidates a fair, accessible, and inclusive experience – regardless of age, color, disability, gender identity, marital status, maternity, national origin, pregnancy, race, religion, sex, sexual orientation, or status as a protected veteran. When applying and interviewing with Braze, we want you to feel comfortable showcasing what makes you *you*.
We know that sometimes different circumstances can lead talented people to hesitate to apply for a role unless they meet 100% of the criteria. If this sounds familiar, we encourage you to apply, as we'd love to meet you.
OUR AI\-POWERED BRAZE RECRUITMENT PROCESS
At Braze, we're committed to a fair and transparent candidate experience. To help our recruitment teams focus on what matters most — the person behind each application — we use AI\-assisted tools at certain stages of our recruitment process.
This includes using AI to analyze the experience, skills and qualifications in your application materials to help with screening and prioritizing candidates. Such screening may amount to a form of solely automated decision\-making. We also use AI for administrative support, like scheduling and recording interviews and summarizing interview notes. Our recruiting teams remain responsible for all hiring decisions and are involved throughout the process.
Depending on where you are located, you may have the right to request further information about how AI is used in our recruitment process, to opt out of AI\-assisted review, to request a manual review of any decision made or to contest a decision.
Please contact us at [email protected] for any requests or questions.To find out more about our hiring process, check out this page.
Notice Regarding Automated Employment Decision Tool (NYC Local Law 144\)
Our use of AI during the application review process may include the use of automated employment decision tools. Pursuant to New York City Local Law 144, for roles based in New York City, or if you reside in New York City, you have the right to request an alternative selection process or a reasonable accommodation instead of AI\-assisted review. Please submit any such request to our Talent Acquisition team at [email protected] promptly after applying. A summary of the most recent bias audit results for such tool is available here*.*
*Please see our* *Candidate Privacy Policy* *for more information on how Braze processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise any privacy rights.*
Salary Context
This $132K-$232K range is below the median for AI Product Manager roles in our dataset (median: $185K across 167 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 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Braze, 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 $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($182K) sits 16% below the category median. Disclosed range: $132K to $232K.
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.
Braze AI Hiring
Braze has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span New York, NY, US, Austin, TX, US. Compensation range: $202K - $232K.
Location Context
AI roles in Austin pay a median of $214,343 across 143 tracked positions.
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 4,317 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).
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 4,317 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.
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