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About This Role
This isn't a normal marketing job.
### The Company:
Sendbird is on a mission to build the AI workforce of tomorrow. For over a decade, we built the infrastructure behind conversations—chat, voice, video, messaging APIs. We became the \#1 CPaaS platform for in\-app communications. 4,000\+ brands trust us. 7 billion messages flow through our platform every month. 300 million monthly active users.
We powered conversations for DoorDash, Match Group, Noom, Yahoo Sports, Rakuten, and thousands of others. We were good at what we did. Really good.
We also saw it early: AI would fundamentally reshape how businesses talk to customers. The infrastructure we'd spent a decade building—the plumbing—would become commoditized. The value would move up the stack, into intelligence, into experience, into outcomes.
We had a choice: protect what we built, or reinvent ourselves.
We chose reinvention.
December 2024: We made the full pivot of our company. A complete strategic shift toward AI\-first customer experience.
February 2025: We launched our AI agent for enterprise CX—built on a decade of conversation data and infrastructure, now with intelligence on top.
November 2025: We rebranded the product line to Delight.ai. The name reflects our belief: AI's real promise isn't efficiency or cost savings. It's restoring what customer experience lost somewhere along the way—the feeling of being understood, of being genuinely cared for. We want every customer to feel delighted. Not satisfied. Delighted.
### The Product:
Delight.ai is the AI concierge for customer experience. Most AI agents forget you the moment the conversation ends. Ours doesn't. Delight.ai builds memory over time, learns preferences, and connects context across every channel—chat, SMS, email, voice, WhatsApp—without losing the thread. We're building AI that makes customers feel understood, seen, and remembered.
### Why AI\-First Full\-Stack Marketer:
Traditionally, this role would be called Product Marketer or Customer Marketer. Depends on which silo you sit in. We don't believe in those silos anymore.
AI collapses the boundaries between roles. A single person with the right tools can now do customer research, shape positioning, write a case study, edit a video, build a landing page, and measure performance—all in a week. What used to require five specialists and three rounds of feedback can now be shipped by one person with taste, curiosity, and the right AI stack.
Speed wins in AI markets. The landscape moves too fast and we need people who can own the full journey—from insight to asset to distribution—without waiting.
The best marketers have always been generalists. The skills that matter—understanding customers, understanding products, synthesizing patterns, communicating clearly—are universal. Specialization was an artifact of complexity. AI dissolves that complexity.
So we're not hiring a Product Marketer. We're not hiring a Customer Marketer.
We're hiring an AI\-First Full\-Stack Marketer—someone who can stretch across the entire journey, move fast, and use AI to punch above their weight.
### The Role:
This role reports to the Product Marketing Lead. This team is building a modern storytelling engine at Delight.ai—powered by our customers and accelerated by AI. We're not looking for someone who "knows how to use ChatGPT." We're looking for someone who's already rewired how they work, and uses AI to do things that weren't possible before.
We're looking for a marketer who can find the best customer stories, the best product angles, and craft them into narratives that resonate—then get them in front of the people who matter. You'll be part customer marketer, part product marketer, full\-time storyteller.
### You might be this person if:
- You've used Claude Code, Claude Cowork, Cursor, Windsurf, Replit, Lovable, or other vibe coding tools
- You've experimented with Runway, Midjourney, Pika, or other AI creative tools
- You learn new tools in days, not months, and you've taught yourself something no one asked you to learn
- You move at a pace that makes other people uncomfortable
- You have the agency and curiosity to will an idea into existence
- You can talk to a customer for 30 minutes and walk away with a story worth telling
- You notice what makes a product feel good—and what makes it feel off
- You go down rabbit holes—and come back with something useful
### You need to have:
- 4\+ years in marketing or product management
- B2B experience
- Work you can point to and say "I made that"
### What you'll actually do:
- Understand things
+ Talk to customers. Dig into the product. Know the market cold.
+ Find the stories hiding in plain sight—the ones no one's told yet.
- Make things
- + Case studies, videos, social content, sales decks—whatever the story needs.
+ Move 10x faster with AI.
+ Ship, learn, iterate. Repeat.
- Tell the story
+ Craft narratives that make people stop scrolling, lean in, remember.
+ Get the right stories in front of the right people at the right time.
+ Make customers proud to be part of our story. Make prospects wish they were.
### Why Sendbird:
We're a team of builders and thinkers that refuse to optimize for comfort. We're building the AI agent platform for customer experience —and we intend to own the category.
### What We Offer Includes (but is not limited to)
- 20 days PTO, 13 paid US company holidays, 7 sick days, 1 volunteer day, plus 2 rest/rejuvenation and birthday days off
- Company subsidized medical, dental, vision insurance
- Flexible spending accounts
- Parental leave
- Life and disability insurance
- *Be Your Best Self*: An annual stipend of $3,500 (prorated after 3 months) for expenses ranging from professional development classes and training, to personality assessments, gym memberships, books, fitness classes, mental health services, and massages
### Pay Transparency
For cash compensation, we set standard ranges for all roles based on function, level, and geographic location. To determine our ranges, we utilize a variety of compensation data benchmarked against similar\-stage growth companies. A reasonable estimate of the current salary range for this role is $140,000 \- $180,000 . This range is specific to the San Francisco Bay market. We consider several factors when making final compensation decisions including, but not limited to, skill sets, experience and training, licensure and certifications, and other business and organizational needs which may cause your specific offer to vary from the amount listed above.
### Flexible Work Policy
We offer a flexible work schedule at Sendbird. We also value collaboration and relationship building. With those values in mind, we require all employees within an hour's commute range of their local office to gather with their team in the office three days per week as a minimum. Some of our roles require a more frequent in\-office schedule. Please work with your manager to understand the office time requirements for your position.
### What diversity and inclusion mean to us
There is no such thing as a perfect candidate and the best employees come from a wide range of backgrounds, experiences, and skill sets. Sendbird is a place where everyone can learn and grow. We respect, promote, and encourage diversity for equal employment opportunities and encourage you to apply if this role excites you.
### About Sendbird
Combining omnichannel AI and battle\-tested, award\-winning communication APIs, Sendbird enables businesses to build AI agents and meaningful customer connections at scale. Trusted by 4,000\+ leading apps—including DoorDash, Match Group, Noom, and Yahoo Sports—Sendbird powers over 7 billion conversations every month, offering exceptional reliability, security, and compliance that meet enterprise\-level demands.
Headquartered in California, Sendbird is backed by ICONIQ, SoftBank, Tiger Global, Y Combinator, and other reputable investors.
Salary Context
This $140K-$180K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At SendBird, 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 Required
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($160K) sits 27% below the category median. Disclosed range: $140K to $180K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
SendBird AI Hiring
SendBird has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Mateo, CA, US. Compensation range: $180K - $180K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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