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About This Role
#### What's in it for you?
Ready to make a serious impact? Millions of people already rely on Calendly, and we're still in the midst of exciting product growth — it's a fantastic time to join us. Everything you'll work on here will accelerate your career to the next level. If you want to learn, grow, and do the best work of your life alongside the best people you've ever worked with, then we hope you'll consider allowing Calendly to be a part of your professional journey.
A day in the life of a Director, Brand Influence, Advocacy \& AI Discovery
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What's so great about working on Calendly's Marketing team?
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We are the team behind how millions of people discover, understand, and fall in love with Calendly. From shaping our brand narrative to mobilizing customers and employees as authentic advocates, our Marketing team drives the awareness and affinity that fuels Calendly's growth. We move fast, think boldly, and are deeply committed to telling stories that matter — and we're looking for a leader to help us do it at scale.
What's in it for you?
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Millions of people rely on Calendly every day, and we're transforming from a scheduling platform into an AI\-powered meeting lifecycle platform. This role will shape how customers, communities, and AI\-powered discovery engines find, trust, and recommend Calendly.
Why do we need you?
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We are looking for a Director of Brand Influence, Advocacy \& AI Discovery to define how Calendly builds trust, authority, and discoverability in an AI\-first world. You will own Calendly's customer advocacy strategy and key distribution channels (social and PR/Communications), while expanding the function to include AI\-powered discovery, ensuring customer voices, community engagement, reviews, thought leadership, and trusted third\-party validation strengthen how buyers—and increasingly AI platforms like ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot—discover and recommend Calendly's new and existing products. Working closely with Brand Design, Content, Growth Marketing, Product Marketing, Product, Customer Success, and Lifecycle Marketing, you will build a modern advocacy engine that fuels awareness, acquisition, activation, expansion, retention, and long\-term brand authority.
On a typical day, you will:
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- Define and execute Calendly's long\-term customer advocacy and AI discovery strategy.
- Define and manage channel roadmaps for social media and PR/Communications
- Build and scale customer advocacy programs including reviews, references, testimonials, ambassadors, speakers, referrals, and creator programs.
- Develop a customer evidence engine that transforms customer success into trusted digital assets including reviews, user\-generated content, tutorials, community discussions, and executive advocacy.
- Partner with Growth, SEO, and Content teams to advance Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO).
- Increase Calendly's visibility across AI\-powered discovery platforms including ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot.
- Expand Calendly's presence across review sites (ie, G2, Trustradius), Reddit, LinkedIn, YouTube, podcasts, industry communities, and partner ecosystems.
- Lead employee and executive advocacy initiatives that amplify authentic brand voices.
- Partner with Product Marketing, Growth Marketing, Product, Customer Success, and Lifecycle Marketing to support product launches, adoption, and expansion.
- Lead, coach, and develop the advocacy, social, and PR team while establishing clear operating rhythms and measurable outcomes.
- Measure performance using traditional channel\-specific metrics alongside emerging AI discovery metrics.
Basic Qualifications
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- 12\+ years of experience in Brand Marketing, Customer Marketing, Community, Product Marketing, Growth Marketing, or Customer Advocacy.
- Experience building and scaling advocacy or community programs within a B2B SaaS organization.
- Strong understanding of Product\-Led Growth (PLG) and customer\-led growth strategies.
- Experience building programs from 01 while scaling mature functions.
- Exceptional cross\-functional leadership and executive communication skills.
- Strong analytical mindset with experience defining KPIs and measuring business impact.
Preferred Qualifications
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- Experience in a high\-growth B2B SaaS company ($100M\+ ARR preferred).
- Knowledge of AI\-powered search ecosystems and emerging Answer Engine Optimization (AEO) and Generative Engine Optimization (GEO), including relevant software solutions for tracking.
- Experience partnering with SEO, Organic Growth, Content Strategy, and Product Marketing teams.
- Experience with advocacy platforms such as Influitive, Gainsight, Khoros, or similar.
- Experience leveraging review platforms, online communities, creator ecosystems, and third\-party trust signals to drive growth.
Success Metrics
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- Customer participation and advocacy engagement
- Review quality and velocity across G2 and other review sites
- Brand share of voice and sentiment
- AI recommendation share and AI citation frequency
- Growth in trusted third\-party mentions
- Customer\-generated content growth
- Community engagement and digital authority
The ranges listed above are the expected annual base salary for this role, subject to change.
Calendly takes a number of factors into consideration when determining an employee's starting salary, including relevant experience, relevant skills sets, interview performance, location/metropolitan area, and internal pay equity.
Base salary is just one component of Calendly's total rewards package. All full\-time (30 hours/week) employees are also eligible for our Top Performer Bonus program (or Sales incentive), equity awards, and competitive benefits.
Calendly uses the zip code of an employee's remote work location, or the onsite building location if hybrid, to determine which metropolitan pay range we use. Current geographic zones are as follows:
- Tier 1: San Francisco, CA, San Jose, CA, New York City, NY
- Tier 2: Chicago, IL, Austin, TX, Denver, CO, Boston, MA, Washington D.C., Philadelphia, PA, Portland, OR, Seattle, WA, Miami, FL, and all other cities in CA.
- Tier 3: All other locations not in Tier 1 or Tier 2
If you are an individual with a disability and would like to request a reasonable accommodation as part of the application or recruiting process, please let your Recruiter know when first connecting with them. Calendly is registered as an employer in many, but not all, states. If you are located in Alaska, Delaware, Hawaii, Idaho, Iowa, Montana, Nebraska, North Dakota, Rhode Island, South Dakota, and West Virginia, you will not be eligible for employment. Note that all individual roles will specify location eligibility.
All candidates can find our Candidate Privacy Statement here
Candidates residing in California may visit our Notice at Collection for California Candidates here: Notice at Collection
This role may require occasional travel for company events, team collaboration, or offsites.
Salary Context
This $223K-$279K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Calendly, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($251K) sits 17% above the category median. Disclosed range: $223K to $279K.
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.
Calendly AI Hiring
Calendly has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $279K - $279K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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 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).
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 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.
Frequently Asked Questions
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